Anticipatory Traumatic Reaction: Outcomes Arising From Secondary Exposure to Disasters and Large-Scale Threats

Abstract

Two studies, with a total of 707 participants, developed and examined the reliability and validity of a measure for anticipatory traumatic reaction (ATR), a novel construct describing a form of distress that may occur in response to threat-related media reports and discussions. Exploratory and confirmatory factor analysis resulted in a scale comprising three subscales: feelings related to future threat; preparatory thoughts and actions; and disruption to daily activities. Internal consistency was .93 for the overall ATR scale. The ATR scale demonstrated convergent validity through associations with negative affect, depression, anxiety, stress, neuroticism, and repetitive negative thinking. The scale showed discriminant validity in relationships to Big Five characteristics. The ATR scale had some overlap with a measure of posttraumatic stress disorder, but also showed substantial separate variance. This research provides preliminary evidence for the novel construct of ATR as well as a measure of the construct. The ATR scale will allow researchers to further investigate anticipatory traumatic reaction in the fields of trauma, clinical practice, and social psychology.

Keywords

anticipatory traumatic reaction disaster indirect trauma media PTSD threat trauma

Reports of disasters and large-scale threats are more accessible than ever before. Whereas once people had limited exposure to details of large-scale traumatic events, modern media such as television and the Internet allow many people to now witness actual footage of violence, destruction, and human suffering—often in real time (Jain, 2010; Kaplan, 2008; Slone & Shoshani, 2010). Geography no longer presents a barrier; whether a disaster occurs on the other side of the street or on the other side of the world, people learn about it and see the effects. The increasing use of techniques such as rolling coverage by some news outlets promotes even higher levels of disaster awareness (Jain, 2010) and consequent discussions—in real life and on social media—may increase people’s perceptions of threat (Lachlan, Spence, & Nelson, 2010).

Recent research has found that people exposed to horrific events via media experience negative reactions (Houston, 2009; Pfefferbaum et al., 2014; Slone & Shoshani, 2010). Although some research highlights the resilience of people following this type of indirect exposure to trauma, showing that heightened stress responses may return quickly to baseline levels (e.g., Matt & Vázquez, 2008), many studies have found that people who are exposed to a traumatic event primarily via mass media—and with no connection to direct victims—can experience reactions similar to those directly subjected to trauma (e.g., Dougall, Hayward, & Baum, 2005; Pfefferbaum et al., 2000). Studies have found strong links between disaster-related media exposure and negative psychological outcomes such as heightened anxiety (Schuster et al., 2001), fear and depression (Lachlan, Spence, & Seeger, 2009), increased levels of threat perception (Maeseele, Verleye, Stevens, & Speckhard, 2008), aggression or stress-related behaviors (Argyrides & Downey, 2004; Vlahov et al., 2002), and posttraumatic stress (PTS) symptoms (Pfefferbaum et al., 2000; Pfefferbaum, Pfefferbaum, North, & Neas, 2002; Schlenger et al., 2002). A meta-analysis by Houston (2009) found a significant association between terrorism-related media exposure and PTS symptoms. Similarly, a research synthesis by Pfefferbaum et al. (2014) showed that correlational studies support the association between disaster-related media exposure and negative outcomes such as PTS symptoms, stress responses, fear, and depression.

Although longitudinal research has also confirmed a link between disaster-related media consumption and negative psychological outcomes (Cohen et al., 2006; Kennedy, Charlesworth, & Chen, 2004; Otto et al., 2007; van Zelst, de Beurs, & Smit, 2003), the majority of research has been cross-sectional and correlational and, as such, has not allowed for inferences of causality. To help clarify the question of whether media exposure to disasters or large-scale threats can trigger negative outcomes, Hopwood and Schutte (2017) conducted a meta-analysis of experimental studies. An overall large standardized effect size of 1.61 indicated that the consumption of media relating to disasters or large-scale violence had a significant effect on negative psychological outcomes and supported the contention that this type of secondary exposure to trauma may play a causal role in negative outcomes, at least transiently.

If, as suggested by these results, short-term, one-off media exposure to reports of disasters and large-scale threats can trigger negative outcomes, however briefly, then it is possible that multiple incidences of such exposure across time may have a cumulative effect. Hopwood and Schutte’s (2017) meta-analysis found that studies from communities that had previously been impacted by traumatic events similar to those portrayed reported larger effect sizes for negative outcomes post media exposure. Studies from the Middle East—which has historically experienced greater numbers of war and terror-related events—displayed larger effect sizes than did studies conducted in other global regions. Other research has indicated that incidences of trauma across the lifetime may render people more vulnerable to certain types of mental health problems, including posttraumatic stress disorder (PTSD), mood disorders, anxiety disorders, and substance abuse (Kubany et al., 2000). These results support the proposal that multiple incidences of trauma exposure may increase a person’s susceptibility to negative outcomes following cues of threat or potential trauma.

There has been debate among academics as to whether media can act as a conduit for trauma (Houston, 2009). Although the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM-5; American Psychiatric Association [APA], 2013) has amended the previous version (4th ed., DSM-IV-TR; APA, 2000) to explicitly state that media consumption cannot constitute exposure to trauma for a diagnosis of PTSD, numerous studies have found links between media exposure and increased scores on self-report PTSD scales (Pfefferbaum et al., 2014). Given that media is no longer considered an adequate antecedent for PTSD, perhaps these scales are—at least in part—capturing a different form of distress. As explained by Slone and Shoshani (2010), the theory of protection motivation (Rogers, 1983) can be used to understand how perceptions of threat from news reports may evoke anxiety. This anxiety may promote a need to defend self or others, which may in turn lead to anger.

Another theory that helps provide an explanatory scaffold for the reactions people experience following media reports is exemplification theory (Zillmann, 2002). Exemplification theory is grounded in the adaptive process of categorization used by human beings to simplify the constant stream of information from the environment (Hayes-Roth & Hayes-Roth, 1977). The theory asserts that people use exemplars—for instance, a selection of salient events—to make implicit decisions about the broader category being represented, including event prevalence (Zillmann, 2002). Research has shown that the way in which the media presents events—particularly, the grouping of particular cases and the emotionality employed—can exert a powerful influence on the way in which people perceive the underlying issues (Zillmann & Brosius, 2012).

Given the propensity for news reports associated with large-scale negative events or threats (such as natural disasters, terrorist attacks, etc.) to include numerous scenes or detailed descriptions of human suffering and to recap similar recent events (Jain, 2010; Kaplan, 2008), exemplification theory predicts that news consumers may be inclined to both overestimate the prevalence of similar events and also perceive an inflated risk to self and others (Spence et al., 2017). Furthermore, studies suggest that the exemplification effects may persist or even strengthen over time (Gibson & Zillmann, 1994). If, due to media reports, people overestimate the risk of negative events, this will evoke an inappropriate level of future-focused anxiety.

Given the ubiquity of threat-related media content together with numerous studies linking media exposure and adverse psychological effects, it is important to identify mechanisms by which negative reactions arise. If the types of symptoms previously identified in the literature cannot be attributed to PTSD (as indicated by the DSM-5), perhaps another construct can better account for these. Given that brief reports of large-scale threats and violence may trigger perceptions of threat and other negative reactions (Hopwood & Schutte, 2017), and that distress-evoking reports may remain salient and generate risk perception (Spence et al., 2017), we propose that longer term exposure to this type of material, either via media or social discussions, may produce a qualitatively different type of reaction for some people. We contend that this form of distress is rooted in uncertainty and the fear of future events that may manifest as feelings of vulnerability, anger, or a need to protect and defend. For those affected by this type of distress, there may be a substantial adverse impact on levels of functioning and well-being. We have termed this potential psychological construct anticipatory traumatic reaction (ATR).

ATR consists of future-focused distress related to safety concerns stemming from exposure to threat-related media or social discussions. Although ATR is conceptualized as sharing some similarities with PTSD symptoms—particularly functional disruptions such as intrusive thoughts, disturbed sleep, and trouble concentrating—ATR does not entail any direct exposure to an adverse event nor exposure to someone directly affected by trauma. Whereas PTSD symptoms or subclinical PTS reactions involve responses to a previously experienced event, ATR involves future-focused anxiety including maladaptive feelings and thoughts about reduced safety and security for self and others due to concern that similar events to those reported may be experienced personally in the future. Furthermore, we propose that people with ATR will—as predicted by protection motivation theory (Rogers, 1983)—feel the need to protect and defend themselves and those they care about. This might involve forms of mental rehearsal, warning others, seeking out further information about the feared event, or taking concrete actions such as stockpiling emergency supplies.

Because we conceptualize ATR as a construct involving negative feelings and maladaptive cognitions, we expect that higher levels of ATR will be related to more negative affect and greater depression. Furthermore, given our contention that ATR involves worry, rumination, and intrusive thoughts, we propose that higher levels of ATR will be related to higher anxiety, higher stress, and more repetitive negative thinking (RNT) as conceptualized by McEvoy, Moulds, and Mahoney (2014). Given that high neuroticism—a personality dimension indicative of emotional instability—has been associated with depression and anxiety in many previous studies (e.g., Clark, Watson, & Mineka, 1994; Griffith et al., 2010), it is also likely that higher levels of ATR will be related to higher levels of neuroticism.

Because positive affect and negative affect are separate constructs (i.e., not existing at opposite ends of the same continuum; Watson, Clark, & Tellegen, 1988), we have no reason to expect ATR to be related to levels of positive affect. Also, although we conceptualize ATR as a construct that may remain fairly stable across time, we contend that it will be unrelated to established dimensions of personality (apart from neuroticism). We have no reason to expect that ATR will be significantly related to extraversion (a sociable and energetic disposition), openness to experience (associated with originality and creativity), agreeableness (a prosocial orientation), or conscientiousness (a predisposition toward organization and goal-directed behavior).

As with many established psychological constructs, individual differences may influence levels of ATR. Previous research has shown that females are more likely than males to be adversely affected following exposure to threat-related media reports (Baum, Rahav, & Sharon, 2014; Lachlan et al., 2010). We examined whether women would be more susceptible to ATR than men.

Eysenck’s (1983) inoculation theory suggests that older people, in response to challenges across their lifetimes, have developed greater resilience, which protects them from the adverse effects of further trauma. Although this theory has been supported by some correlational research (e.g., Shrira, Palgi, Hamama-Raz, Goodwin, & Ben-Ezra, 2014; Schlenger et al., 2002), other studies have found that older people may be more susceptible than younger people to adverse psychological outcomes (e.g., Kubany et al., 2000; Kun, Han, Chen, & Yao, 2009). We examined whether older adults would be more vulnerable than younger adults to ATR.

Two studies investigated the construct of ATR. Study 1 aimed to develop a reliable and valid psychometric measure of ATR and provide preliminary evidence for ATR in a general population. We expected that responses to items reflecting possible components of ATR would show evidence of a latent construct that may consist of subfactors reflecting different aspects of such reactions. Study 2 aimed to provide further validation of ATR as a distinct construct—including demonstrating separate variance from PTSD. This study also aimed to explore links between ATR and levels of exposure and engagement with media reports and social discussions of threat-related material.

The hypotheses underlying the research were the following:

Hypothesis 1: ATR will show convergent validity though associations with conditions one might expect to be related to ATR: (a) Higher levels of ATR will be associated with higher levels of negative affect, depression, anxiety, stress, negative thinking, and neuroticism. (b) Higher levels of ATR will be associated with more hours of and greater engagement with indirect exposure to threat-related material.

Hypothesis 2: ATR will show discriminant validity, showing little overlap with theoretically unrelated constructs: (a) ATR will have little association with positive affect and (b) with the personality constructs of extraversion, agreeableness, openness, and conscientiousness.

Hypothesis 3: ATR will be a distinct construct from PTSD, having separate variance from PTS symptoms.

Hypothesis 4: Age and gender will be associated with differences in ATR: (a) Women will show higher levels of ATR than will men and (b) older adults will show higher levels of ATR than will younger adults.

Study 1

The purpose of this study was to search for preliminary evidence of ATR in a general population and to develop a reliable and valid psychometric measure for this form of distress, hypothesized to occur in response to media reports or social discussions related to large-scale negative events or threats.

Method

Generation of the Initial Pool of Items

The initial pool of 30 items intended to assess ATR was constructed on a dual underlying scaffold of (a) emotions, cognitions, and behaviors commonly implicated in secondary exposure to traumatic events in past research and (b) the diagnostic criteria for PTSD from the DSM-5—phrased in terms of what might constitute subclinical symptoms in response to indirect trauma. Accordingly, there were items that related to emotions, cognitions, and behaviors in combination with diagnostic criteria. For example, this dual scaffold resulted in items such as intrusion symptoms (e.g., “Thinking about the possibility of an event like this occurring in my life makes it hard to concentrate on daily tasks”), avoidance symptoms (e.g., “I avoid places or things that might put me at risk from this type of event”), alterations in cognitions and mood (e.g., “Thinking about the event makes me feel generally pessimistic about the future”), and alterations in arousal and reactivity (e.g., “I have trouble sleeping”), mapping to DSM-5 309.81 Criteria B, C, D, and E, respectively (APA, 2013). Several items overlapped with two of the diagnostic criteria.

Each of the authors independently evaluated the items for relevance and clarity. Five experts in the areas of trauma psychology and trauma-related media exposure (identified from published peer-reviewed articles and contacted via e-mail) then evaluated the initial pool of items. Based on the feedback from the experts, some items were replaced and others were revised to improve clarity or emphasize the future-focused orientation of ATR. For example, the item regarding trouble sleeping, which was originally “I have trouble sleeping due to concerns about how this type of event might affect my family or myself in the future” was shortened due to one expert’s concern about ambiguity and the conflation of cognitions and arousal. Some negatively worded items were included to reduce bias associated with acquiescence (DeVellis, 1991). Thirty items assessing reactions to secondary exposure to disasters and large-scale threats were retained in the preliminary scale. Participants were asked to indicate their usual reaction to media reports or social discussions of large-scale negative events or threats of such events. They responded to each item on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Participants

A stratified sample, according to age and sex, in line with population demographics from the Australian Bureau of Statistics (2015), of 630 adult Australian participants from the general population were recruited via Qualtrics Panels. After elimination of straight-line responders, 601 participants remained. Of these, 305 were women and 296 were men. Ages of participants ranged from 18 to 85 years, with a mean of 47.13 (SD = 16.95).

Measures

The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988)

This 20-item affect measure comprises subscales assessing the extent to which people tend to experience positive and negative feelings. The scale was administered with general instructions (“. . . to what extent you generally feel this way”) previously shown to yield measures of affect with trait-like stability (Leue & Lange, 2011). Items comprise words reflecting feelings, such as interested, excited, and strong for positive affect and distressed, upset, and irritable for negative affect. The scale uses a 5-point scale from 1 (very slightly or not at all) to 5 (extremely). The PANAS has been widely used in clinical, longitudinal, and experimental research; scores are calculated on two separate subscales because research has confirmed that positive and negative affect are independent dimensions (see Watson, 2000). Studies have demonstrated high internal consistency of the PANAS, between .85 and .88, and have supported the validity of the measure (Watson, 2000). In the current sample, the internal consistency was excellent, with .92 for positive affect and .94 for negative affect.

Depression, Anxiety, Stress Scales–21-item version (DASS-21; Lovibond & Lovibond, 1995)

The DASS-21 is a self-report tool designed to measure depression, anxiety, and stress. Participants are asked to read statements about feelings and experiences and indicate on a 4-point scale from 0 (did not apply to me at all) to 3 (very much, or most of the time) how much each item has applied to them “over the past week.” The DASS-21 has been extensively used in psychological research and has been reported to show good internal consistency in non-clinical samples (.80 to .91; Sinclair et al., 2012). The scale has shown good validity in assessing the psychological dimensions of depression, anxiety, and stress (Daza, Novy, Stanley, & Averill, 2002). In the current sample, the internal consistency was .95 for depression, .91 for anxiety, and .92 for stress.

The Perseverative Thinking Questionnaire (PTQ; Ehring et al., 2011)

The PTQ is a brief 15-item measure of RNT. The PTQ was designed to measure RNT transdiagnostically (not specific to any psychological disorder). Ehring et al. (2011) found the PTQ possessed good to excellent internal consistency for the overall scale (α = .95) and for the three subscales of core characteristics (repetitiveness, intrusiveness, difficulties with disengagement; α = .94), perceived unproductiveness (α = .83), and capturing mental capacity (α = .86), which describes the extent to which people’s repetitive thoughts stop them from focusing on other things. Convergence with other established measures of RNT has also demonstrated the validity of the PTQ (Ehring et al., 2011). In the current sample, internal consistency of the PTQ was .96 for the overall scale, and .95, .82, and .89 for the three subscales (core characteristics, perceived unproductiveness, and capturing mental capacity, respectively).

The Big Five Inventory (BFI; John, Donahue, & Kentle, 1991)

The BFI is a 44-item scale designed to assess personality traits across five broad domains—extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The items are presented as simple short phrases and participants are asked to indicate the extent to which they agree or disagree that each item reflects their personal characteristics. Answers are recorded on a 5-point scale from 1 (disagree strongly) to 5 (agree strongly). The BFI has been shown to possess good internal consistency, retest reliability, and validity (Benet-Martínez, & John, 1998; DeYoung, 2006). In the current sample, the internal consistency for the subscales was .83 for extraversion, .77 for agreeableness, .85 for conscientiousness, .88 for neuroticism, and .76 for openness.

Procedure

An online questionnaire was generated using Qualtrics survey software. The survey consisted of four demographic questions, the 30 initial items for ATR, PANAS, DASS-21, PTQ, and BFI. All participants were asked to respond to the demographic questions and the items for ATR. Approximately equal numbers of randomly assigned participants (n ≈ 150) were asked to complete one of the other four measures (PANAS, DASS-21, PTQ, or BFI).

In line with recommendations by Fabrigar, Wegener, MacCallum, and Strahan (1999), we used random assignment (SPSS Version 22) to divide the sample into two equal groups—301 sets of responses for exploratory factor analysis (EFA) and 300 for confirmatory factor analysis (CFA). To identify latent variables and reduce the number of items to the most parsimonious set, we used SPSS to conduct an EFA. Because we were searching for hypothetical causal factors that would explain the scores on the proposed variables, we used principal axis factoring (Tabachnick & Fidell, 2007). We then conducted a CFA using AMOS (Version 23.0; Arbuckle, 2014) to verify the fit of the measurement model (Fabrigar et al., 1999). Factor scores were calculated for all measures and Pearson correlations were run to assess the convergent and discriminant validity of the ATR scale.

Results

Exploratory Factor Analysis

Initial examination of the data indicated that the data set was suitable for factor analysis; the sample size (n = 301) met Tabachnick and Fidell’s (2007) recommendation of a minimum of 300 cases and Nunnally’s (1978) guideline of at least 10 cases for each item. The intercorrelations between items also appeared sufficient, with an inspection of the correlation matrix revealing many coefficients greater than .30. Two other tests also indicated that the data had adequate factorability; Bartlett’s test of sphericity was significant, p < .0001, and the Kaiser–Meyer–Olkin index was .94, exceeding the recommended minimum of .6.

Examination of the correlation matrix showed three items (Items 8, 14, and 28) with low correlations (<.30) to most other items; Item 8 had only six correlations greater than .30, Item 14 had two, and item 28 none. As recommended by Yong and Pearce (2013), these items were excluded, as they did not form patterned relationships with the other variables. Because Items 8 and 28 were negatively phrased, it is possible that the wording may have caused confusion for some participants. Item 14 (“I avoid media reports because I don’t want to hear any more about this event or type of threat”) was conceptually ambiguous, as we were unsure if this media-avoidance behavior would be associated with high or low levels of ATR; it was initially included in the EFA so we could learn how it interacted with the other variables. The low initial communality values for Items 8, 14, and 28 (at .440, .305, and .292, respectively) suggested that the factor solution would account for little variance in these items and supported our decision to exclude them (Costello & Osborne, 2005).

Principal axis factoring of the remaining 27 items yielded four factors with eigenvalues greater than 1, explaining 43.1%, 6.9%, 3.8%, and 2.6% of the variance, respectively. Although the scree plot was ambiguous, a parallel analysis using permutations of the raw data (Horn, 1965; O’Connor, 2000) indicated a three-factor solution. Given that parallel analysis has been identified as one of the most accurate methods for determining the number of factors (Hayton, Allen, & Scarpello, 2004), three factors were retained.

The three-factor solution explained a total of 53.5% of the variance with Factor 1 explaining 43.1%, Factor 2 explaining 6.8%, and Factor 3 contributing 3.6%. To assist with interpretation of the factors, an oblique (Promax) rotation was performed. The pattern matrix revealed that most items loaded substantially on only one factor, but there were some items that proved problematic for obtaining a simple structure solution. Four items (Items 12, 16, 24, and 26) had high cross-loadings of greater than .3 with a difference of less than .2 between primary and secondary loadings; therefore, as recommended by Yong and Pearce (2013), these items were removed. Similarly, three items (Items 2, 3, and 18) had low primary loadings (<.5) and did not contribute to a meaningful solution, so were therefore also excluded (Yong & Pearce, 2013). Given that these items had comparable counterparts still in the scale (capturing similar experiences and loading in a meaningful way), the integrity of the content was retained (Costello & Osborne, 2005). The items and their primary loadings are shown in Table 1.

Table 1.

Primary Factor Loadings for Exploratory Factor Analysis of Anticipatory Traumatic Reaction With Promax Rotation.

Item	Subfactor	Loading
9. The possibility of an event like this affecting my family or me makes me angry.^d,e	Feelings	.79
22. I do not feel concerned that an event like this will affect my family or me in the future.^a,d (R)	Feelings	−.76
4. I find myself thinking that the world is becoming increasingly dangerous.^a,d	Feelings	.71
13. I feel frightened that something similar to this event may happen to me or to people I care about.^d	Feelings	.70
6. I feel distressed at the possibility that this type of event might affect either myself or my family and friends in the future.^b,d	Feelings	.69
1. I feel anxious that something similar to this event might occur in my life.^b,d	Feelings	.67
11. Thinking about this report makes me feel like my family and I may not be safe.^a,d	Feelings	.65
5. Thinking about the event makes me feel generally pessimistic about the future.^a,d	Feelings	.62
25. I feel horrified that a similar type of event could affect me or those I care about in the future.^d	Feelings	.61
*16. The possibility of an event like this affecting my family or me in the future makes me feel helpless.^a,d	Feelings	.58
15. The possibility of an event like this happening to me or to people I care about makes me feel tense.^e	Feelings	.56
*26. I don’t think about the possibility of an event like this occurring in my life.^a,b (R)	Feelings	−.53
*2. I find myself repeatedly thinking about the possibility of an event like this occurring in my life.^b	Feelings	.47
17. I think of plans to help keep my family and myself safe if something like this happens to us.^e	Preparation	.86
10. I plan or carry out actions to protect myself or others from possible future events like this (e.g., stocking emergency supplies).^e	Preparation	.75
21. I warn family and friends to avoid places and things that might put them at risk from this type of event.^c,e	Preparation	.60
27. I search for further information about this event or similar events or threats (e.g., news stories or information on the Internet).^a,e	Preparation	.58
29. I discuss the event or threat with others (e.g., in person or by social media) to make them aware of possible future risks.^a,e	Preparation	.54
*12. I find myself thinking about how I would cope if something similar to this event were to occur in my life.^b,e	Preparation	.53
7. I discuss the possibility of an event like this occurring in the future with family or friends.^a	Preparation	.52
23. I find myself imagining this type of event happening to my family and friends or to me.^b	Preparation	.52
*18. The details of the report pop back into my head later, even when I’m preoccupied with something else.^b,e	Preparation	.44
*3. I avoid places or things that might put me at risk from this type of event.^a,c	Preparation	.40
*24. This event makes me think of other bad things that could happen to me or to people I care about in the future.^b,d	Preparation	.34
19. Thinking about the possibility of an event like this occurring in my life makes it hard to concentrate on daily tasks.^b,e	Disruption	.75
30. I find it hard to get on with things I should be doing because I can’t help thinking about what would happen if this type of event affected me or my family.^b,e	Disruption	.74
20. I have trouble sleeping.^a,e	Disruption	.58

Note. Items 8, 14, and 28 (not shown) were removed due to low correlations with other items prior to exploratory factor analysis. The items removed from the final scale are denoted with an asterisk (*). (R) Denotes reverse scored items.

Items derived from emotions, behaviors, and cognitions related to secondary exposure to trauma identified in past research. Superscripts b to e denote the derivation of items based on DSM-5 diagnostic criteria for Posttraumatic Stress Disorder 309.81: ^bCriterion B—intrusion symptoms. ^cCriterion C—avoidance symptoms. ^dCriterion D—alterations in cognitions and mood. ^eCriterion E—alterations in arousal and reactivity.

The EFA was performed again on the remaining 20 items, and both the parallel analysis and the scree plot suggested three factors should be retained. The resultant three-factor solution was meaningful and—with the exception of a complex factor loading for Item 23 (“I find myself imagining this type of event happening to my family and friends or to me,” loading at .39 on Factor 2 and .36 on Factor 3)—produced a clear interpretable structure. Ten items loaded on the first factor and these included Item 9 (“The possibility of an event like this affecting my family or me makes me angry”) and Item 4 (“I find myself thinking that the world is becoming increasingly dangerous”). The first factor was interpreted as feelings and beliefs related to future threat. Seven items loaded on the second factor, including Item 17 (“I think of plans to help keep my family and myself safe if something like this happens to us”) and Item 21 (“I warn family and friends to avoid places and things that might put them at risk from this type of event”). The second factor was therefore interpreted as preparatory thoughts and actions designed to protect self and others. Only three items loaded on the third factor, including Item 19 (“Thinking about the possibility of an event like this occurring makes it hard to concentrate on daily tasks”) and Item 20 (“I have trouble sleeping”), and this factor was interpreted as disruptions to daily activities.

The percentage of nonredundant residuals with absolute values greater than .05 was 13%. This was well below the cutoff of 50%, indicating that the model is a good fit for the data (Yong & Pearce, 2013). The 20-item scale with instructions for respondents is shown in the appendix.

Confirmatory Factor Analysis

To verify the results of the first phase of the study, we performed a CFA on the 20 retained items of the ATR scale. Results of the maximum likelihood estimates model provided support for both a unidimensional model of ATR and the previously derived three-factor structure of ATR. Motivation indices were examined and covariances relaxed between error terms for items within a single latent factor that showed high values (motivation index > 10), as these suggested correlated residuals. According to McDonald and Ho (2002), good model fit is indicated by comparative fit index (CFI) and Tucker–Lewis index (TLI) values of .95 or greater and a root mean square error of approximation (RMSEA) of .05 or less; an acceptable model fit has CFI and TLI values of .90 or greater and a RMSEA of .08 or less. Given these guidelines, the resultant three-factor model consisting of the latent variables Feelings, Preparation, and Disruption provided an acceptable fit for the data, with χ²(158) = 351.20, p < .0001; RMSEA = .06; CFI = .94; and TLI = .92. All ATR items had significant loadings on the latent variables, ranging from .44 to .86. Using the same criteria of relaxing covariances (where appropriate) for error terms with high motivation indices, AMOS yielded a one-factor model with an acceptable but less suitable fit than the three-factor model, with χ²(149) = 368.13, p < .0001; RMSEA = .07; CFI = .93; and TLI = .91.

Reliability

The full sample (N = 601) provided internal consistency information for the overall ATR scale and the three subscales. The internal consistency for the overall scale was excellent, with a Cronbach’s alpha of .93. Likewise, the subscales of Feelings, Preparation, and Disruptions all showed good internal consistency with alpha values of .91, .85, and .76, respectively. The correlations between factors ranged from .58 to .64.

Tests of Convergent and Discriminant Validity

To test the hypothesis that ATR would demonstrate convergent validity by showing associations with theoretically related constructs, we used Pearson correlation coefficients to assess the associations between the ATR scale and negative affect, depression, anxiety, stress, and RNT. As predicted, higher levels of ATR were associated with more negative affect, r(152) = .46, higher depression, r(144) = .33, higher anxiety, r(144) = .46, and higher stress, r(144) = .45 (all ps < .001). The ATR–Disruptions factor was more strongly correlated with anxiety, r(144) = .59, p < .001, and stress, r(144) = .56, p < .001, than were the ATR–Feelings and ATR–Preparation factors (all rs < .4 but significant at p < .001). Higher levels of ATR were associated with more negative thinking at r(143) = .56, p < .001; the ATR–Disruptions factor was associated with negative thinking at r(143) = .65, p < .001, and with negative thinking related to mental capacity, at r(143) = .71, p < .001. Higher levels of ATR were also associated with higher levels of neuroticism, r(154) = .35, p < .001.

To test the hypothesis that ATR would show discriminant validity, we used Pearson correlation coefficients to assess associations between ATR scores and positive affect and also between ATR and the four personality constructs that were theorized to be conceptually distinct (extraversion, agreeableness, conscientiousness, and openness). ATR was not associated with positive affect, r(152) = .15, p = .06, or with extraversion, r(154) = .10, p = .20, or agreeableness, r(154) = −.05, p = .55. There were weak-to-moderate significant associations between ATR and conscientiousness, r(154) = −.18, p = .02, and between ATR and openness, r(154) = .25, p = .002.

Tests for Individual Differences

To test the hypothesis that women would show greater levels of ATR than men, an independent samples t test was conducted. A significant gender difference showed women (M = 62.27, SD = 13.89) having higher levels of ATR than men (M = 57.54, SD = 14.39), t(599) = −4.09, p < .001, 95% confidence interval, CI, [−6.99, −2.46]. To test the hypothesis that older adults would show higher levels of ATR than would younger adults, we examined the correlation coefficient, which indicated that, in contrast to the expected direction of effect, ATR was significantly higher in young adults, r(599) = −.21, p < .001.

Discussion

The results of the study indicated that ATR in response to media exposure to and social discussions of disasters and large-scale threats may be a distinct and measureable construct. As expected, the results of EFA and CFA indicated that ATR may be a latent construct composed of emotions, cognitions, and behaviors. This latent construct includes subfactors of feelings related to future threat; preparatory thoughts and actions designed to protect self and others; and disruption to daily activities. These subscales reflect a meaningful and logical division of the proposed components of the ATR construct.

ATR showed preliminary convergent validity through associations with theoretically related outcomes. As predicted, higher levels of ATR were related to more negative affect, greater depression, stress and anxiety, more negative thinking, and higher levels of neuroticism. The subscales of ATR showed somewhat different relationships with these other variables, signaling some utility of examination of the separate subfactors.

Preliminary findings relating to discriminant validity showed that ATR shared little variance with theoretically unrelated constructs; as predicted, ATR was unrelated to positive affect, extraversion, and agreeableness. There was a weak negative relationship between ATR and conscientiousness and a slightly stronger positive relationship between ATR and openness (but still only represented 6% of shared variance). It is possible that the relationship with openness may reflect a greater willingness on the part of people to imagine themselves (or their families) in the adverse situations depicted in media or conversations – and this type of propensity may be related to ATR.

There is evidence for ATR in a general population of Australian adults. In terms of individual differences that may affect ATR, as predicted, women may be more susceptible than men. In contrast to our prediction regarding the effects of age, it appears that older adults may be somewhat less vulnerable to ATR than younger adults.

The present study provided initial support for the concept of ATR and also provided information regarding a scale that can be used to assess ATR. Internal consistency of the final items assessing overall ATR was high and internal consistency of the three subscales reflecting the subfactors was good. The combination of convergent and discriminant validity information set a foundation for construct validity.

Study 2

We conceptualized ATR as people’s usual reactions when exposed to large-scale negative events or threats via media or social discussions. The aim of this study was to further validate the scale and provide evidence that ATR is a distinct construct.

Given the construct of ATR contains a subscale for preparatory thoughts and behaviors, and that some of these behaviors involve seeking out further media or discussions related to large-scale negative events or threats, we predicted that higher levels of ATR would be related to higher levels of exposure and engagement with these types of media reports and social discussions.

Despite the fact that some ATR scale items were generated in line with the criteria associated with PTSD, we propose that ATR and PTSD are distinct constructs, differing in both origin and temporal focus. For this reason, we predicted that ATR would be shown to have separate variance from PTS symptoms.

Method

Participants

A stratified sample, according to age and sex, in line with population demographics from the Australian Bureau of Statistics (2016), of 104 adult Australian participants from the general population were recruited via Qualtrics Panels. After removal of four straight-line responders and four extreme outliers, 96 participants remained. Of these, 47 were women and 49 were men. Ages of participants ranged from 18 to 76 years, with a mean age of 45.75 years (SD = 17.37).

Measures

Anticipatory Traumatic Reaction

This is a 20-item measure of adverse reactions to media presentations and social discussions relating to large-scale negative events or threats (see Study 1).

Life Events Checklist (LEC-5) and PTSD Checklist (PCL-5; Weathers, Marx, Friedman, & Schnurr, 2014)

The LEC-5 is a 17-item checklist that screens for exposure to potentially traumatic events. It was designed to assess exposure to events deemed capable of evoking PTSD (Criterion A events for PTSD, as outlined in the DSM-5), but has also been shown to have validity as a stand-alone measure of trauma (Gray, Litz, Hsu, & Lombardo, 2004). For each traumatic event listed, participants are asked to consider their “entire life” and indicate one or more of the following in regards to their level of exposure: “Happened to me”; “Witnessed it happen to someone else”; “Learned about it happening to a family member or friend”; “Part of my job”; “Not sure”; “Doesn’t apply to me.”

The 20-item PCL-5 is used to measure the severity of PTSD symptoms. Participants are asked to keep in mind their “worst event” from the previous scale (the LEC-5). They are then asked to read statements related to symptoms and indicate on a 5-point scale, from 0 (not at all) to 4 (extremely), the extent to which they have been “bothered by that problem in the past month.” Although the scale can be used to determine a provisional PTSD diagnosis, it can also be used (and it was here) as a relative measure of severity of symptoms associated with PTSD across all clusters: intrusion, avoidance, negative alterations in cognitions and mood, arousal, and reactivity (Weathers et al., 2014). Symptom severity was calculated by summing scores across all 20 items, giving a total between 0 and 80 for each participant.

DASS-21 (Lovibond & Lovibond, 1995)

This scale is a self-report tool designed to measure depression, anxiety, and stress (See Study 1). In the current study, a composite measure of general distress was calculated by averaging the total scores for each of the three dimensions (Henry & Crawford, 2005). The internal consistency of the scale was very high for the composite measure, with a Cronbach’s alpha of .96.

Exposure and engagement questions

Six questions assessed habits and attributes relating to exposure to media reports and discussions, which are conceptually central to ATR. Participants reported estimated hours of media exposure per week and their level of engagement with the media reports on a 5-point scale from 1 (I pay very little attention) to 5 (I am completely focused on the reports to the exclusion of everything else). Participants also indicated on a 5-point scale the proportion of news they usually obtain via social media. Two questions asked participants to estimate the number hours spent in social discussions about threatening events per week and their level of engagement in these discussions on a 5-point scale from 1 (I don’t care much about these types of discussions) to 5 (I am extremely interested in these types of discussions). A final question asked participants to indicate their level of concern about world politics on a 5-point scale from 1 (I am not at all concerned) to 5 (World politics represent a major concern for me).

Procedure

An online questionnaire was generated using Qualtrics survey software. The completed survey consisted of two demographic questions (gender and age) followed by the exposure and engagement questions outlined above. The three psychometric measures (ATR, LEC-5/PCL-5, DASS-21) were then presented in counterbalanced order.

Statistical Analyses

All statistical analyses were performed using SPSS software (Version 24).

Results

Reliability

The sample (N = 96) provided internal consistency information for the overall ATR scale and the three subscales. The internal consistency for the overall scale, as assessed by Cronbach’s alpha was .93. The subscales of feelings, preparation, and disruptions also showed good reliability, with alpha values of .91, .85, and .74, respectively.

Intercorrelations

Visual inspection of the frequency histogram revealed ATR scores to be normally distributed, with a mean score of 60.30 (SD = 15.21). Pearson correlations were calculated for all key variables and the results are shown in Table 2. Higher levels of ATR were associated with higher levels of engagement across media and social discussions. For the subscales, the association with level of engagement was significant for the ATR preparation subscale but not for feelings or disruptions. Overall, ATR was unrelated to total hours of exposure to media and social discussions per week. In terms of the ATR subscales, only preparation showed a significant (positive) association with hours of exposure.

Table 2.

Descriptive Statistics and Intercorrelations for Key Variables.

Variable		1	2	3	4	5	6	7	8	9	10	11	12
1.	Gender^a	—
2.	Age	−.24*	—
3.	Hours of exposure	.02	−.27**	—
4.	Level of engagement	−.09	.04	.24*	—
5.	News from social media	.25	−.31**	.31**	.12	—
6.	Concern about world politics	−.08	.07	.19	.49**	.02	—
7.	ATR	.22*	−.19	.14	.20*	.29**	−05	—
8.	ATR–feelings subscale	.24*	−.16	.07	.18	.21*	−.07	.94**	—
9.	ATR–preparation subscale	.14	−.16	.21*	.24*	.33**	.03	.90**	.74**	—
10.	ATR–disruptions subscale	.17	−.21*	.12	.04	.21*	−.10	.69**	.53**	.53**	—
11.	PTS symptoms	.12	−.39**	.24*	.03	.35**	−.11	.52**	.41**	.46**	.57**	—
12.	General distress	.24*	−.27**	.21*	−.06	.31**	−.15	.58**	.48**	.49**	.66**	.81**	—
	M	—	45.75	10.17	3.35	2.17	3.30	60.30	33.23	20.07	7.00	20.03	9.21
	SD	—	17.37	8.14	0.94	0.93	1.06	15.21	8.39	5.95	2.88	17.21	9.56
	Range (Min-Max)	—	18-76	0-40	1-5	1-4	1-5	20-93	10-50	7-35	3-15	0-69	0-39

Note. ATR = anticipatory traumatic reaction. n = 96. Hours of exposure is the total hours per week of exposure to threat-related media reports and social discussions. Level of engagement is the average level of engagement across media reports and social discussions.

Dummy coded (male = 1; female = 2).

p < .05. **p < .01.

People with higher levels of ATR obtained a higher proportion of their news from social media. This association with proportion of news obtained from social media was significant for all three subscales of ATR, but was strongest for the preparation subscale.

To examine whether ATR is a distinct construct from PTSD, the correlation and R² value were calculated for the relationship between ATR and PTSD. The result, r(94) = .52, p < .001, showed that people with higher ATR also reported more PTS symptoms. The R² value indicated that ATR and PTSD shared 27% of the variance. The large proportion of separate variance (73%) supports the contention that ATR is a distinct and separate construct from PTSD.

Tests for Individual Differences

A t test assessed whether levels of ATR differed across genders. The result indicated that women (M = 63.66, SD = 15.40) scored significantly higher on ATR than did men (M = 57.08, SD = 14.45), t(94) = −2.16, p = .03, 95% CI [−12.63, −0.53].

ATR was not significantly associated with age; however, there was a trend toward ATR being higher among younger participants, r(94) = −.19, p = .067.

Supplementary Mediation Analysis

To explore a possible causal pathway in the relationship between age and ATR, we conducted a supplementary regression-based mediation analysis (using Model 4 in PROCESS; Hayes, 2016). Because younger age was associated with greater ATR and with more social media use, we examined whether social media might explain the trend toward ATR being higher among younger participants. The analysis showed a significant indirect effect of age on ATR through the proportion of news obtained via social media, b = −0.07, SE = 0.04, 95% CI [−0.17, −0.01]. The completely standardized indirect effect was −0.08, SE = 0.04, 95% CI [−0.19, −0.01] and the results are shown in Figure 1. It should be noted that although Path c (the total effect of age on ATR before accounting for social media) was not significant according to the correlational analyses conducted in SPSS (p = .067), the robust bootstrapping procedure (using 5,000 samples) in PROCESS yielded a significant total effect of age on ATR (p = .04).

Figure 1.

Model of age as a predictor of anticipatory traumatic reaction (ATR), mediated by proportion of news obtained via social media, with the indirect effect based on 5000 bootstrap resamples.

Discussion

The ATR scale was constructed to measure people’s adverse reactions to media reports or social discussions of large-scale negative events or threats. The internal consistency for the overall scale was excellent, with a Cronbach’s alpha of .93. Similarly, the subscales—feelings, preparation, and disruptions—all showed good internal consistency, with alpha values of .91, .85, and .74, respectively.

The normal distribution of ATR scores in the sample indicates that this future-focused type of distress may be occurring at moderate to high levels for a significant number of people in a general population of Australian adults. This additional evidence of the potential prevalence of ATR supports the need to investigate mechanisms through which this phenomenon may be occurring.

The hypothesis that higher levels of ATR would be associated with higher levels of, and greater engagement with, indirect exposure to large-scale negative events and threats was only partially supported. As predicted, people with higher levels of ATR reported higher levels of engagement across media reports and social discussions. It is possible that people who engage more with threat-related material are more susceptible to ATR. Conversely, people who are more affected by ATR may find themselves engaging more with this type of material when they encounter it. This may be particularly true for people who feel the need to prepare or defend against negative outcomes, as ATR–preparation is the only subscale that reflected the significant association. The true nature of this relationship may be cyclical; people with higher ATR might engage more with the threat-related material, which in turn may magnify ATR.

The association between higher engagement and higher levels of ATR is consistent with research relating to exemplification theory. Aust and Zillmann (1996) showed that emotionally laden media items remained more salient for participants than did emotionally neutral items and were associated with greater perceptions of risk. Thus, perhaps people’s level of engagement reflects the nature of the media reports they consume (i.e., how emotive the media material is). It is also possible that both the propensity to engage with material showing human distress and a person’s susceptibility to ATR might be linked to another psychological construct, such as empathy. Future research should further investigate the links between ATR, engagement, and empathy.

The other part of the engagement–exposure hypothesis was not supported; people with higher levels of ATR did not report more hours of exposure to threat-related material per week. Given the construct of ATR is conceptually linked to media consumption and social discussions, this finding at first seemed counterintuitive. However, reexamination of the instructions on the ATR scale provides some clarification. We asked people to indicate their “usual response” when exposed to reports or discussions of large-scale negative events or threats—that is, their typical response per unit of news. Given that the unit of exposure is encapsulated within the scale, there should be no expectation of a relationship between ATR and hours of exposure per week.

A further factor that might influence the association between hours of exposure to threat-related material and ATR is variation in individual approach or avoidance tendencies; some people with higher scores on ATR might approach media and social discussions relating to trauma, whereas others might avoid this type of threat-related material. Given this, the relationship between ATR and hours of exposure might be balanced toward zero. It would be reasonable to expect people who approach media and discussions to be higher on the preparation subscale of ATR, and in fact, the data support a moderate, significant correlation between hours of exposure and ATR preparation.

It is likely that differences in news reports or social discussions may also affect people’s levels of ATR. In line with exemplification theory, differences in the reports—including levels of emotionality and the way in which newsworthy events are compiled—may generate different perceptions of risk. For instance, the aggregation of a number of terror attacks within one report may exert greater exemplification effects than the report of a single incident. Given this proposed mechanism of threat salience, the heterogeneity of media and social discussions also helps explain the nonsignificant relationship between amount of report exposure and ATR.

Another interesting finding with regard to media exposure involved people’s use of social media to obtain news reports. People who gathered a greater proportion of their news in this way showed higher levels of ATR. This was true for overall ATR scores and for scores on the three subscales of feelings, preparation, and disruptions; but the association was strongest for the preparation subscale. Given that gathering news from social media requires an intentional act (not just a state of passive reception, as may be the case when watching a television news report), the stronger relationship with the preparation factor of ATR seems consistent with the active nature of the behaviors identified in this subscale (e.g., I search for further information . . .; I plan or carry out actions to protect myself or others . . .).

In terms of individual differences, as predicted, women may be at greater risk of ATR than men. Our prediction that older adults would show higher levels of ATR than younger adults was not supported. There was a trend in the opposing direction, suggesting older adults may be less susceptible to ATR than younger adults.

To explore potential mechanisms involved in the trend of younger adults showing higher levels of ATR, we examined whether social media use (i.e., the proportion of news obtained via social media) might play a mediating role. This analysis was based on the observation that younger people obtained a higher proportion of their news from social media and that higher levels of ATR were associated with higher levels of social media use. The a posteriori prediction regarding the mediating effect of social media was supported, indicating that a larger proportion of social media use may be one mechanism through which younger adults experience higher levels of ATR.

The ATR scale was partially constructed based on subclinical symptoms related to the criteria for PTSD in the DSM-5. However, the scale instructions asked people to respond to the items based on their “usual reactions to reports or discussions of large-scale negative events, or threats of such events,” and most of the items are phrased with a future-focused orientation. Therefore, we predicted that ATR would be shown to be a distinct construct. This hypothesis was supported, with ATR having a large proportion of separate variance from PTS symptoms. Although ATR and PTSD share some similarities, particularly, the disturbances to daily functioning that may occur due to intrusive thoughts, these differ in origin and temporal focus.

Because some items in the ATR and PTSD scales are similar, if participants fail to keep in mind the instructions (i.e., for PTSD, answering with regard to a previously traumatic event, or, for ATR, answering with regard to reports or discussions of large-scale negative events), the results may fail to accurately represent the construct of interest. This phenomenon may account for some of the shared variance found here.

General Discussion

The purpose of this research was to investigate a novel construct, ATR, a type of future-focused distress experienced in response to media reports or social discussions of large-scale negative events or threats. Despite media exposure being excluded from the DSM-5 as a sufficient criterion for experiencing trauma antecedent to PTSD (APA, 2013), findings from many studies have suggested that this type of indirect exposure may evoke adverse psychological reactions, including forms of distress similar to PTSD, at least at subclinical levels (Houston, 2009; Pfefferbaum et al., 2014).

Although many studies of indirect trauma in the extant literature were cross-sectional or longitudinal in nature, a meta-analysis by Hopwood and Schutte (2017) examined experimental studies of negative reactions in response to media reports and found a significant and strong effect, supporting a causal link. Given that short-term exposure to media has been shown to evoke negative psychological reactions, we proposed that cumulative exposure to this type of material might generate a qualitatively different form of distress, anchored in negative anticipation of large-scale trauma or disasters. Our contention was also based on research identifying exemplification effects from media exposure, whereby people may overestimate prevalence and risk of events made salient in media reports and discussions (Spence et al., 2017).

In Study 1, based on past research evidencing emotional, cognitive, and behavioral responses to indirect trauma and the DSM-5 criteria for PTSD (APA, 2013), we generated a pool of items intended to assess the novel construct of ATR. EFA and CFA results suggested that ATR represents a latent construct related to reactions to indirect exposure to traumatic events. The analyses further indicated that ATR may contain three subfactors: feelings related to future threat, preparatory thoughts and actions designed to protect self and others, and disruption to daily activities.

The subscales reflect a meaningful division of the types of feelings, cognitions, and behaviors that may arise among people who experience distress in reaction to secondary exposure to disaster and threat. The first subfactor and subscale relates to anxiety—the feelings and beliefs that a person may experience when basic assumptions of safety and security become threatened. The second subfactor and subscale relates to cognitions and behaviors predicted by protection motivation theory (Rogers, 1983)—a need to mentally mobilize and rehearse or prepare for difficult times and to protect self and others. The third subfactor and subscale comprises reactions similar to the intrusive symptoms associated with PTSD; however, it represents disruptive thoughts of future threats prompted by media exposure that impair daily functioning.

The internal consistency for the overall scale was excellent with Cronbach’s alphas of .93 for both Studies 1 and 2. The feelings subscale also showed excellent internal consistency with alpha values of .91 for both studies. The preparation subscale showed good internal consistency, with alpha values of .85 for both studies. Finally, the disruptions subscale demonstrated adequate to good internal consistency, with alpha values of .76 and .74.

As predicted in Hypothesis 1, ATR displayed evidence of convergent validity; ATR was related to constructs one would expect to be related. These included negative affect, depression, anxiety, stress, neuroticism, and negative thinking (Hypothesis 1a). We also expected ATR to be related to threat-related media reports and social discussions. It was hypothesized that higher levels of ATR would be associated with both longer durations of exposure to, and higher levels of engagement with, media reports and social discussions related to large-scale negative events and threats (Hypothesis 1b). Although higher levels of ATR were not related to more hours of exposure to threat-related material per week, the other dimension of exposure—level of engagement—was shown to be related to ATR; people with higher levels of ATR reported higher levels of engagement across threat-related media and social discussions.

As predicted in Hypothesis 2, ATR has evidence of discriminant validity, showing little overlap with the theoretically unrelated constructs. There was no relationship between ATR and positive affect (Hypothesis 2a), and no relationships between ATR and the Big Five personality traits of extraversion, and agreeableness (Hypothesis 2b). There was a weak relationship between ATR and conscientiousness and a slightly stronger relationship between ATR and openness, but the overlap with these constructs was negligible.

Although the construct of ATR was partially conceptualized in line with symptoms associated with subclinical PTSD, ATR also involves a variety of other emotions, behaviors, and cognitions. Furthermore, ATR does not involve the experience of previous trauma; instead, it consists of a form of future-focused worry about events that have been made salient in news reports and social discussions. Although sharing similar intrusive features—which may manifest as difficulty concentrating or completing tasks and trouble sleeping—ATR and PTSD differ in both origin and temporal focus. In ATR, intrusive reactions will be related to concerns about possible future events, whereas in PTSD, similar reactions will arise due to re-experiencing of, or intrusive thoughts of, a previously experienced traumatic event. Also, although PTSD and ATR both exhibit reactions related to avoidance, people with PTSD seek to avoid reminders of past trauma, whereas people with ATR seek to protect significant others from possible future danger by encouraging avoidance of potentially dangerous environments. In ATR, this is an example of a preparatory behavior enacted in anticipation of future trauma.

There are some similarities between ATR and PTSD and also possible bidirectional effects. Each condition might feed into the other and be exacerbated by media reports, social discussions, or other threat-related stimuli (Ahern, Galea, Resnick, & Vlahov, 2004; Nelson et al., 2009). However, ATR is distinct from PTSD in that (a) ATR occurs in reaction to media reports and social discussions of nonpersonal events and (b) ATR-related distress arises due to anticipation of personal future threat, as opposed to PTSD-related distress, which arises due to past experience of trauma.

Given these important conceptual differences, we hypothesized that ATR would be a distinct construct from PTSD, having separate variance from PTS symptoms, and this was supported. We contend that some of the findings in the extant literature related to adverse psychological reactions in the wake of media exposure to threat-related material might be better accounted for by ATR than by subclinical PTSD. If the PTSD items were asked about in the context of media reports, the PTSD scales may have captured elements of ATR.

Analyses in both Studies 1 and 2 supported our prediction of gender effects, showing that women had higher levels of ATR than did men. This could indicate that women are more vulnerable to this form of distress or this result may simply be an artifact of a greater propensity on the part of women to report (or be aware of) such reactions. This gender difference is also consistent with research on exemplification theory, indicating that females are more susceptible than men to exemplification effects due to media reports (Spence et al., 2017). Also, as suggested by Spence et al. (2017), gender could be acting as a proxy for empathy in these results. The same type of effect could be occurring here; it is possible that people who are more empathetic will engage more closely with media and discussions pertaining to threat (and human suffering) and will be more susceptible to overestimating prevalence and risk of possible future events.

In contrast to the prediction that older people would be more susceptible to ATR than younger people, Study 1 found that older people experienced less ATR than did younger people and there was a trend toward replication of this result in Study 2. This result aligns with the stress inoculation hypothesis (Eysenck, 1983), which contends that age may be a protective factor against stress reactions. Further to this, the finding in Study 2 that social media use (i.e., the proportion of news obtained via social media) acted as a mediating path between age and ATR offers one possible mechanism to help explain why younger people may experience higher levels of ATR than older people. Given the current heightened prevalence of social media use among younger people, there may be a generational effect in terms of social media use that renders younger people more susceptible to ATR. The cross-sectional nature of the research, however, does not allow a definite causal inference; longitudinal research will be needed to test whether social media use may lead to higher levels of ATR.

Limitations and Future Research

Although the sample sizes for Study 1 are considered adequate for factor analysis, larger heterogeneous samples would provide a more robust test of the factor model (Costello & Osborne, 2005). Future studies might replicate these findings with larger samples and clarify relationships with gender and age. It would be useful to assess the strength and prevalence of ATR in a variety of populations.

Future research should investigate environmental factors and further individual differences that might increase or reduce susceptibility to ATR. The current research captured only a snapshot of participants’ behaviors relating to media and social discussions of negative events or threats; future studies should use longitudinal methods to investigate cumulative exposure as a possible causal factor. Longitudinal studies with larger samples could also assess the temporal stability of ATR and experimental research could be used to investigate what types of interventions might mitigate ATR.

Conclusion

A number of studies have suggested that media reports and social discussions of threatening events may evoke negative psychological outcomes for some people (Houston, 2009; Pfefferbaum et al., 2014) and a recent meta-analysis of experimental studies has indicated a causal link (Hopwood & Schutte, 2017). Given these findings, intense or cumulative exposure to this type of threat salience may evoke a qualitatively different future-focused form of distress, termed ATR. The present research provides initial support for the existence of ATR and presents initial validity evidence for the construct as measured by the ATR scale. Furthermore, the distribution of the scores in a general population suggests that this form of distress may be occurring for many people.

Given the current heightened salience of threat in the global community—due to factors such as increasing levels of terrorism, political unrest, and economic uncertainty—the construct of ATR is extremely relevant. The omnipresence of media reporting and online discussions mean that threats may be exemplified in ways that cause an overestimation of risk for adverse events and an increase in prevalence of ATR. The preparation component of ATR, which involves people actively seeking out more threat-related material might cause a positive feedback process in ATR, exacerbating the effects. Given the increasing presence of information media in society, it is possible that this form of distress may become more prevalent in the coming years, representing a substantial burden for both individuals and communities.

People who are affected by ATR may begin to view the world as unsafe and the future as bleak. Such a worldview might translate into a lack of self-efficacy to effect change and people may be less inclined to interact with their environment in positive ways. Terror management theory (Becker, 1975, as cited in Miller & Landau, 2005) asserts that feelings of pervasive threat and mortality salience will result in increased adherence to cultural worldviews, a result of which can be greater division between societal groups. This type of sentiment may lead to an exacerbation of racism and other maladaptive in-group–out-group behaviors. Societal costs may include reduced community morale, a deficit of hope, and increased prevalence of mental illness.

Given the potential burden of ATR on both individuals and communities, more research should be conducted to identify individual differences that make people vulnerable to ATR. The ATR scale could be used by clinicians to identify patients who—due to heightened levels of this form of distress—may be experiencing functional decline and impaired subjective well-being. It is also possible that ATR might exacerbate existing psychological conditions such as PTSD, generalized anxiety, and major depression and further research should explore possible links.

The ATR scale could be used in experimental research to help determine the types of interventions that would best ameliorate the condition. Furthermore, a version of the ATR scale could be constructed to measure children’s reactions to media reports and parental discussions of negative world events. If children are affected by ATR, this could have a significant impact on their critical axiomatic beliefs about safety and security, impeding optimal psychological development, and perhaps resulting in later deficits.

Additional research is needed to develop a causal model for ATR that will include risk and buffering factors. Awareness of ATR and its assessment will provide a platform for further research into this construct and its applications in the fields of trauma, social psychology, and clinical practice.

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Ahern

Galea

Resnick

Vlahov

(2004). Television images and probable posttraumatic stress disorder after September 11: The role of background characteristics, event exposures, and perievent panic. Journal of Nervous and Mental Disease, 192, 217-226. doi:10.1097/01.nmd.0000116465.99830.ca

Aust

C. F.

Zillmann

(1996). Effects of victim exemplification in television news on viewer perception of social issues. Journalism & Mass Communication Quarterly, 73, 787-803. doi:10.1177/107769909607300403

Australian Bureau of Statistics. (2015). Australian demographic statistics table 8: Estimated resident population, by age and sex (Data file 31010DO002_201506). Retrieved from http://www.abs.gov.au/AUSSTATS

Australian Bureau of Statistics. (2016). Australian demographic statistics table 8: Estimated resident population, by age and sex (data file 31010DO002_201606). Retrieved from http://www.abs.gov.au/AUSSTATS

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., Text Rev.). Washington, DC: Author. doi:10.1176/appi.books.9780890423349

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. doi:10.1176/appi.books.9781585624836.jb07

Arbuckle

J. L.

(2014). Amos (Version 23.0) [Computer software]. Chicago, IL: IBM SPSS.

Argyrides

Downey

J. L

. (2004). September 11: Immediate and long term effects on measures of aggression, prejudice, and person perception. North American Journal of Psychology, 6, 175-187. Retrieved from http://mwc.sagepub.com.ezproxy.une.edu.au/content/1/1/50

Baum

Rahav

Sharon

(2014). Heightened susceptibility to secondary traumatization: A meta-analysis of gender differences. American Journal of Orthopsychiatry, 84, 111-122. doi:10.1037/h0099383

10.

Benet-Martínez

John

O. P.

(1998). Los Cinco Grandes across cultures and ethnic groups: Multitrait–multimethod analyses of the Big Five in Spanish and English. Journal of Personality and Social Psychology, 75, 729-750. doi:10.1037/0022-3514.75.3.729

11.

Clark

L. A.

Watson

Mineka

(1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology, 103, 103-116. doi:10.1037/0021-843X.103.1.103

12.

Cohen

Kasen

Chen

Gordon

Berenson

Brook

White

(2006). Current affairs and the public psyche: American anxiety in the post 9/11 world. Social Psychiatry and Psychiatric Epidemiology, 41, 251-260. doi:10.1007/s00127-006-0033-7

13.

Connery

H. S.

(2003). Acute symptoms and functional impairment related to September 11 terrorist attacks among rural community outpatients with severe mental illness. Harvard Review of Psychiatry, 11, 37-42. doi:10.1080/10673220303939

14.

Costello

A. B.

Osborne

J. W.

(2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9. Retrieved from http://pareonline.net/getvn.asp?v=10&n=7

15.

Daza

Novy

D. M.

Stanley

M. A.

Averill

(2002). The Depression Anxiety Stress Scale-21: Spanish translation and validation with a Hispanic sample. Journal of Psychopathology and Behavioral Assessment, 24, 195-205. doi:10.1023/A:1016014818163

16.

DeVellis

R. F.

(1991). Scale development: Theory and applications. Newbury Park, CA: Sage.

17.

DeYoung

C. G.

(2006). Higher-order factors of the Big Five in a multi-informant study. Journal of Personality and Social Psychology, 91, 1138-1151. doi:10.1037/0022-3514.91.6.1138

18.

Dougall

A. L.

Hayward

M. C.

Baum

(2005). Media exposure to bioterrorism: Stress and the anthrax attacks. Psychiatry, 68, 28-42. doi:10.1521/psyc.68.1.28.64188

19.

Ehring

Zetsche

Weidacker

Wahl

Schonfeld

Ehlers

(2011). The Perseverative Thinking Questionnaire (PTQ): Validation of a content-independent measure of repetitive negative thinking. Journal of Behavior Therapy and Experimental Psychiatry, 42, 225-232. doi:10.1016/j.jbtep.2010.12.003

20.

Eysenck

H. J.

(1983). Stress, disease, and personality: The “inoculation effect.” In Cooper

C. L.

(Ed.), Stress research (pp. 121-146). New York, NY: Wiley.

21.

Fabrigar

L. R.

Wegener

D. T.

MacCallum

R. C.

Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299. doi:10.1037/1082-989X.4.3.272

22.

Gibson

Zillmann

(1994). Exaggerated versus representative exemplification in news reports perception of issues and personal consequences. Communication Research, 21, 603-624. doi:10.1177/009365094021005003

23.

Gray

M. J.

Litz

B. T.

Hsu

J. L.

Lombardo

T. W.

(2004). Psychometric properties of the Life Events Checklist. Assessment, 11, 330-341. doi:10.1177/1073191104269954

24.

Griffith

J. W.

Zinbarg

R. E.

Craske

M. G.

Mineka

Rose

R. D.

Waters

A. M.

Sutton

J. M.

(2010). Neuroticism as a common dimension in the internalizing disorders. Psychological Medicine, 40, 1125-1136. doi:10.1017/S0033291709991449

25.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach [Kindle book]. Retrieved from https://www.amazon.com.au/Introduction-Mediation-Moderation-Conditional-Analysis-ebook/dp/B00F07L1VU

26.

Hayes

A. F.

(2016). PROCESS for SPSS for Mac (Version 2.16.3) [Computer software]. Retrieved from http://processmacro.org/download.html

27.

Hayes-Roth

(1977). Concept learning and the recognition and classification of exemplars. Journal of Verbal Learning and Verbal Behavior, 16, 321-338. doi:10.1016/S0022-5371(77)80054-6

28.

Hayton

J. C.

Allen

D. G.

Scarpello

(2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, 191-205. doi:10.1177/1094428104263675

29.

Henry

J. D.

Crawford

J. R.

(2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44, 227-239. doi:10.1348/014466505X29657

30.

Hopwood

T. L.

Schutte

N. S.

(2017). Psychological outcomes in reaction to media exposure to disasters and large-scale violence: A meta-analysis. Psychology of Violence, 7, 316-327. doi:10.1037/vio0000056

31.

Horn

J. L.

(1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.

32.

Houston

J. B.

(2009). Media coverage of terrorism: A meta-analytic assessment of media use and posttraumatic stress. Journalism & Mass Communication Quarterly, 86, 844-861. doi:10.1177/107769900908600408

33.

Jain

(2010). “Beaming it live”: 24-Hour television news, the spectator and the spectacle of the 2002 Gujarat carnage. South Asian Popular Culture, 8, 163-179. doi:10.1080/14746681003797989

34.

John

O. P.

Donahue

E. M.

Kentle

R. L.

(1991). The Big Five Inventory—Versions 4a and 54. Berkeley: University of California at Berkeley, Institute of Personality and Social Research.

35.

Kaplan

E. A.

(2008). Global trauma and public feelings: Viewing images of catastrophe. Consumption Markets & Culture, 11, 3-24. doi:10.1080/10253860701799918

36.

Kennedy

Charlesworth

Chen

J.-L.

(2004). Disaster at a distance: Impact of 9.11.01 televised news coverage on mothers’ and children’s health. Journal of Pediatric Nursing, 19, 329-339. doi:10.1016/j.pedn.2004.09.003

37.

Kubany

E. S.

Haynes

S. N.

Leisen

M. B.

Owens

J. A.

Kaplan

A. S.

Watson

S. B.

Burns

(2000). Development and preliminary validation of a brief broad-spectrum measure of trauma exposure: The Traumatic Life Events Questionnaire. Psychological Assessment, 12, 210-224. doi:10.1037//1040-3590.12.2.210

38.

Kun

Han

Chen

Yao

(2009). Prevalence and risk factors for posttraumatic stress disorder: a cross-sectional study among survivors of the Wenchuan 2008 earthquake in China. Depression and Anxiety, 26, 1134-1140.

39.

Lachlan

K. A.

Spence

P. R.

Nelson

L. D.

(2010). Gender differences in negative psychological responses to crisis news: The case of the I-35W collapse. Communication Research Reports, 27, 38-48. doi:10.1080/08824090903293601

40.

Lachlan

K. A.

Spence

P. R.

Seeger

(2009). Terrorist attacks and uncertainty reduction: Media use after September 11. Behavioral Sciences of Terrorism & Political Aggression, 1, 101-110. doi:10.1080/19434470902771683

41.

Leue

Lange

(2011). Reliability generalization: An examination of the Positive and Negative Affect Schedule. Assessment, 18, 487-501. doi:10.1177/1073191110374917

42.

Lovibond

P. F.

Lovibond

S. H.

(1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33, 335-343. doi:10.1016/0005-7967(94)00075-U

43.

Maeseele

P. A.

Verleye

Stevens

Speckhard

(2008). Psychosocial resilience in the face of a mediated terrorist threat. Media, War & Conflict, 1, 50-69. doi:10.1177/1750635207087625

44.

Matt

G. E.

Vázquez

(2008). Anxiety, depressed mood, self-esteem, and traumatic stress symptoms among distant witnesses of the 9/11 terrorist attacks: Transitory responses and psychological resilience. Spanish Journal of Psychology, 11, 503-515. doi:10.1017/S1138741600004509

45.

McDonald

R. P.

M. H.

(2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82.

46.

McEvoy

P. M.

Moulds

M. L.

Mahoney

A. E.

(2014). Repetitive negative thinking in anticipation of a stressor. Behaviour Change, 31, 18-33. doi:10.1017/bec.2013.30

47.

Miller

C. H.

Landau

M. J.

(2005). Communication and terrorism: A terror management theory perspective. Communication Research Reports, 22, 79-88. doi:10.1080/0882409052000343543

48.

Nelson

E. C.

Agrawal

Pergadia

M. L.

Lynskey

M. T.

Todorov

A. A.

Wang

J. C.

. . . Goate

A. M.

(2009). Association of childhood trauma exposure and GABRA2 polymorphisms with risk of posttraumatic stress disorder in adults. Molecular Psychiatry, 14, 234-235. doi:10.1038/mp.2008.81

49.

Nunnally

J. C.

(1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.

50.

O’Connor

B. P.

(2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32, 396-402. doi:10.3758/BF03200807

51.

Otto

M. W.

Henin

Hirshfeld-Becker

D. R.

Pollack

M. H.

Biederman

Rosenbaum

J. F.

(2007). Posttraumatic stress disorder symptoms following media exposure to tragic events: Impact of 9/11 on children at risk for anxiety disorders. Journal of Anxiety Disorders, 21, 888-902. doi:10.1016/j.janxdis.2006.10.008

52.

Pfefferbaum

Newman

Nelson

S. D.

Nitiéma

Pfefferbaum

R. L.

Rahman

(2014). Disaster media coverage and psychological outcomes: Descriptive findings in the extant research. Current Psychiatry Reports, 16, 464. doi:10.1007/s11920-014-0464-x

53.

Pfefferbaum

R. L.

North

C. S.

Neas

B. R.

(2002). Does television viewing satisfy criteria for exposure in posttraumatic stress disorder? Psychiatry, 65, 306-309. doi:10.1521/psyc.65.4.306.20242

54.

Pfefferbaum

Seale

T. W.

McDonald

N. B.

Brandt

J. E. N.

Rainwater

S. M.

Maynard

B. T.

. . . Miller

P. D.

(2000). Posttraumatic stress two years after the Oklahoma City bombing in youths geographically distant from the explosion. Psychiatry, 63, 358-370. doi:10.1521/00332747.2000.11024929

55.

Rogers

R. W.

(1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In Cacioppo

Petty

(Eds.), Social psychophysiology: A sourcebook (pp. 153-176). New York, NY: Guilford Press.

56.

Schlenger

W. E.

Caddell

J. M.

Ebert

Jordan

B. K.

Rourke

K. M.

Wilson

D. M.

. . . Kulka

R. A.

(2002). Psychological reactions to terrorist attacks: Findings from the National Study of Americans’ Reactions to September 11. JAMA Journal of the American Medical Association, 288, 581-588. doi:10.1001/jama.288.5.581

57.

Schuster

M. A.

Berry

S. H.

Stein

B. D.

Jaycox

L. H.

Collins

R. L.

Marshall

G. N.

. . . Morrison

J. L.

(2001). A national survey of stress reactions after the September 11, 2001, terrorist attacks. New England Journal of Medicine, 345, 1507-1512. doi:10.1056/NEJM200111153452024

58.

Shrira

Palgi

Hamama-Raz

Goodwin

Ben-Ezra

(2014). Previous exposure to the World Trade Center terrorist attack and posttraumatic symptoms among older adults following Hurricane Sandy. Psychiatry, 77(4), 374-385. doi:10.1521/psyc.2014.77.4.374

59.

Sinclair

S. J.

Siefert

C. J.

Slavin-Mulford

J. M.

Stein

M. B.

Renna

Blais

M. A.

(2012). Psychometric evaluation and normative data for the Depression, Anxiety, and Stress Scales-21 (DASS-21) in a nonclinical sample of U.S. adults. Evaluation & the Health Professions, 35, 259-279. doi:10.1177/0163278711424282

60.

Slone

Shoshani

(2010). Prevention rather than cure? Primary or secondary intervention for dealing with media exposure to terrorism. Journal of Counseling & Development, 88, 440-448. doi:10.1002/j.1556-6678.2010.tb00044.x

61.

Spence

P. R.

Lachlan

K. A.

Westerman

Lin

Harris

C. J.

Sellnow

T. L.

Sellnow-Richmond

D. D.

(2017). Exemplification effects: Responses to perceptions of risk. Journal of Risk Research, 20, 590-610. doi:10.1080/13669877.2015.1100658

62.

Tabachnick

B. G.

Fidell

L. S.

(2007). Using multivariate statistics (5th ed.). Boston, MA: Allyn & Bacon.

63.

van Zelst

de Beurs

Smit

J. H

. (2003). Effects of the September 11th attacks on symptoms of PTSD on community-dwelling older persons in the Netherlands. International Journal of Geriatric Psychiatry, 18, 190. doi:10.1002/gps.758

64.

Vlahov

Galea

Resnick

Ahern

Boscarino

J. A.

Bucuvalas

. . . Kilpatrick

(2002). Increased use of cigarettes, alcohol, and marijuana among Manhattan, New York, residents after the September 11th terrorist attacks. American Journal of Epidemiology, 155, 988-996. doi:10.1093/aje/155.11.988

65.

Watson

(2000). Mood and temperament. New York, NY: Guilford Press.

66.

Watson

Clark

L. A.

Tellegen

(1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070. doi:10.1037/0022-3514.54.6.1063

67.

Weathers

F. W.

Marx

B. P.

Friedman

M. J.

Schnurr

P. P.

(2014). Posttraumatic stress disorder in DSM-5: New criteria, new measures, and implications for assessment. Psychological Injury and Law, 7, 93-107. doi:10.1007/s12207-014-9191-1

68.

Yong

A. G.

Pearce

(2013). A beginners guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9, 79-94. Retrieved from http://www.tqmp.org/Content/vol09-2/p079/p079.pdf

69.

Zillmann

(2002). Exemplification theory of media influence. In Bryant

Zillmann

(Eds.), Media effects: Advances in theory and research (2nd ed., pp. 19-41). Mahwah, NJ: Lawrence Erlbaum. Retrieved from https://books.google.com.au/books?id=KnQXQOP5uoMC&printsec=frontcover&dq=editions:4QNwFJOcWLoC&hl=en&sa=X&ved=0ahUKEwiM6ISIjaTUAhWTNpQKHR8PANgQ6AEIKDAB#v=onepage&q=basal&f=false

70.

Zillmann

Brosius

H. B.

(2012). Exemplification in communication: The influence of case reports on the perception of issues. New York, NY: Routledge.