Predicting resistance to health education messages for cannabis use: The role of rebelliousness,autic mastery,health value and ethnicity

Abstract

The prevalent use of cannabis by adolescents and young adults, combined with the common misconception that smoking cannabis is relatively harmless, warrants the exploration of factors influencing resistance to health education messages about cannabis use. This investigation did so within a Reversal Theory framework. One hundred and thirteen undergraduate students responded to an informative leaflet detailing the health effects of smoking cannabis. The most significant independent predictor of message resistance was frequency of cannabis use, followed by proactive rebelliousness, autic mastery, health value and ethnicity. These findings have implications for the development, design and targeting of cannabis health education programmes.

Keywords

cannabis education messages ethnicity health value personal control rebelliousness resistance

Introduction

Despite pervasive and recurrent health promotion campaigns, risky health behaviours remain commonplace. Cannabis is the most frequently used illicit drug throughout the world (United Nations, 2010). In England and Wales, the British Crime Survey (Flatley et al., 2010) found that 42.9% of adults aged between 16 and 59 had used cannabis in their lifetime, 9.8% had used the drug in the last year, while 6% had taken the drug within the last month. Among young adults aged 16 to 24, the prevalence of cannabis use is higher, where 52.7% reported lifetime use, 23.8% reported use in the last year and 14.1% reported use in the last month. This pervasive use of cannabis testifies to the public perception of cannabis as a relatively harmless drug (Ashton, 2001) and is of concern given research showing that it has harmful effects to both psychological and physical health (Hall and Degenhardt, 2009; Kuepper et al., 2011).

Resistance to health promotion messages

There is now growing evidence to suggest that some individuals may choose to actively resist health education messages and therefore such messages are only minimally effective. The theory of psychological reactance (Brehm, 1966) was one of the first theoretical models to consider resistance. When applied to health education, reactance theory posits that if an individual’s freedom is threatened by health education messages, the individual will attempt to re-establish their freedom by purposely engaging in the behaviour that is being discouraged. The health promotion is met with opposition and paradoxically increases the unhealthy behaviour being discouraged. Rhodewalt and Marcroft (1988) demonstrated that Type A coronary prone individuals are particularly sensitive to psychological reactance and are therefore potentially resistant to health education messages.

More recently, Crossley (2001a, 2001b, 2002a, 2002b, 2004) has investigated the concept of resistance to health promotion, particularly in relation to ‘barebacking’ (unprotected sex in gay men). Crossley theorized that health has become associated with being morally good and well behaved. Some individuals may not wish to be perceived in this way and so actively resist or rebel against the explicit and implicit requirements of repeated health promotion messages. This effect may be greatly amplified in young adults who often wish to be rebellious and non-conformist (Balswick and Macrides, 1975; Tacon and Abner, 1993). Thus, health promotions among young people may actually reinforce the behaviour and achieve the exact opposite of the intended effect.

This interest in the concept of resistance led Crossley (2001a, 2001b, 2002a, 2002b) to develop a health resistance scale, a generic measure of resistance to health promotion messages in different populations. However, the initial item pool consisted of just 22 items and so may not have captured the entirety of the resistance construct. Additionally, respondents were self-selecting therefore the very individuals who would be the target of such a questionnaire may have reacted against participation.

Reversal Theory, risk and health

To investigate the psychological concomitants of resistance to health education messages about cannabis use, a suitable theoretical framework for so doing is provided by Reversal Theory (RT) (Apter, 1982, 2001). This is a general theory of motivation, emotion and personality, suggesting that individuals are dominant for (that is to say, spend most time in) certain psychological states, but may switch or reverse to opposite states in response to situational influence, or in response to behavioural contingencies, or to frustration and satiation. One pair of such states between which the theory proposes such switching, is the rebellious/conformity pair (the former also being known in RT as ‘negativism’) (McDermott, 2001). Apter (1982 and 1989) defined the rebellious state as: ‘wanting, or feeling compelled, to do something contrary to that required by some external agency’. This definition stresses the importance of the actor’s experience in designating what is rebellious behaviour, rather than the externally imposed standards of an observer. So, self-report methods are ideally suited to the measurement of rebelliousness. RT also includes a number of further personality constructs: telic (the need to feel one is achieving, a serious state); paratelic (the need to feel enjoyment, a playful state); arousal avoidance; arousal seeking; autic mastery (the need for personal power and control); alloic mastery (the need to enjoy the power of others by identifying or being part of the team); autic sympathy (the need to be attractive to others); alloic sympathy (the need to care for others); optimism; pessimism; arousablity; and effortfulness. Research with these states has implicated their importance for the prediction of various health behaviours, including smoking cessation (O’Connell et al., 2004), alcohol use (Turner and Heskin, 1998), risk taking/unhealthy behaviours (Klabbers et al., 2009), dangerous sports (Apter and Batler, 1997), unsafe sexual behaviours (Gerkovich, 1997) and cannabis use (Sartori, 2003).

Given the predictive utility of RT constructs within the health domain and given the similarity between rebelliousness and reactance, we report here a study which examines such utility in the context of an elaborated measure of resistance to health promotion about cannabis use. We drew on the previous work of Lanarch and Brown (1997) who investigated the influences of rebelliousness (or ‘negativism’), telic dominance (goal-orientedness) and readiness for change on the resistance to anti-smoking health promotion messages. After reading anti-smoking leaflets, participants responded to an author-devised self-report measure of resistance to health promotion material. It was shown that both high and low frequency smokers were significantly more rejecting of the health promotion messages than non-smokers. Also, for the psychological variables, the results of multivariate analysis were approaching significance, suggesting that individuals who were dominant for rebelliousness were more likely to reject the health promotion.

The study reported here examines the resistance of health education messages about smoking cannabis. The specific hypotheses of this study were that: (1) the most frequent cannabis users will be the most resistant to the health education messages; (2) resistance to such messages will be associated with high rebelliousness, low telic, high autic (self), low mastery dominance, low arousal seeking and low effortfulness; and (3) low health value scores are also hypothesized as being a concomitant of message resistance. To enable this study, a cannabis specific measure of health education resistance was developed and reported here also.

Method

Participants

One hundred and thirteen UK undergraduate psychology students completed the questionnaire (53 male, 60 female, mean age = 23.15, SD = 5.46). Ethnicity was predominantly white (n = 66). Forty-seven per cent (n = 53) considered themselves to be a current cannabis user. Forty-seven per cent (n = 53) reported themselves to be non-cannabis users. The use of other illegal drugs ranged from 1.8% for Heroin and Barbiturates to 16.8% for Ecstasy (Methylenedioxymethamphetamine – viz. MDMA).

Design and materials

The study took the form of a cross-sectional multivariate correlation design, following information provided to students in the form of a brief leaflet.

Independent variables

The Motivational Style Profile (MSP) (Apter et al., 1998) is a questionnaire designed to measure the tendency of individuals to experience a variety of psychological states and ‘dominances’ found within RT. The MSP measures the tendency to experience 14 states: telic (goal-oriented); paratelic (playful); arousal avoidance; arousal seeking; negativism (rebelliousness); conformity; autic mastery (desire for control over others); autic sympathy (desire to be cared for); alloic mastery (desire for others to have power); alloic sympathy (desire to care for others); optimism; pessimism; arousability; and effortfulness. Six dominance scores are calculable from these state measures, specifically: telic dominance; arousal-avoidance dominance; negativism dominance; mastery dominance; autic dominance; and optimism dominance.

A visual inspection of the rebelliousness items within the MSP revealed that only the proactive rebellious state was measured, therefore additional items to measure the reactive form were added, as after McDermott (1986, 1987, 1988): (1) Like to retaliate when treated unfairly; (2) Like to seek revenge when wronged; (3) Like to even the score if humiliated; (4) Dislike taking orders from people in authority; and (5) Like to rebel against unreasonable demands.

The Social Reactivity Scale (also known as the Negativism Dominance Scale) was included given this constitutes a more extended dominance measure of proactive and reactive rebelliousness (Klabbers et al., 2009; McDermott and Apter, 1988).

The Health Value Scale (Lau et al., 1986) was included to measure the value an individual places on their health.

Dependent variable: Health Messages Resistance

The Health Messages Resistance Scale (for Cannabis), entitled ‘Your beliefs about cannabis’ when administered, measures the degree to which health education messages about cannabis use are resisted. In its original form as developed by Lanarch and Brown (1997), it consisted of 39 items. An additional 29 items were generated to accommodate the identification of nine conceptual subscales with six to eight items in each. These nine conceptual subscales were: perceived harmfulness to health; perceived harmfulness of infrequent or low dose; reduction of health risk upon cessation; openness to persuasive health literature; perceived psychological risk; perceived risk of heart disease, lung cancer, bronchitis and accidents; need for social and political intervention; longevity and mortality; and passive smoking. Each item required a response on a five-point Likert scale, with high scores indicating rejection of the health promotion message. Fifteen items were reverse scored as they were worded in the opposite direction.

The health information leaflet

A two-page health information leaflet was produced from a number of sources (Frank, 2010; Hall and Degenhardt, 2009; Maisto et al., 2010; Royal College of Psychiatrists, 2009) conveying information and risk factors associated with smoking cannabis. Specifically, the leaflet addressed the following: what cannabis is derived from and what it is pharmacologically; its psychological and psychomotor effects; the short-term and long-term health effects of smoking cannabis; the effects of smoking cannabis on others; its legal status; and the benefits of giving up use of cannabis.

Results

The Health Messages Resistance: Item analysis and dependent variable subscales development

In order to develop the scale further and to improve the reliability and usability of the scale, preliminary item analyses were performed. A reliability analysis was run on all items, with the 28 items that made the largest contributions to the overall Cronbach’s alpha for the entire scale selected for inclusion in the next stage of scale development. Given the ratio of items to participants was now equivalent to 28:113 (i.e. 1:4), data from these items can then be entered into a principal components analysis with the possibility of a stable and replicable underlying factor structure being determined. Before this however, a scree analysis (as after Cattell, 1966) was carried out to determine the number of factors to extract in the principal components analysis. The scree analysis revealed that two factors could be extracted. Therefore, a two factor principal components analysis specifying varimax rotation was computed. The results of this showed that 15 items loaded exclusively and > .4 on the first factor, while seven items loaded exclusively at > .4 on the second factor, and collectively accounted for just over 50% of the variance in the correlation matrix. In order to produce two concise subscales, only items that had a factor analysis weighting of > .6 were included in each. This cut-off meant that factor one consisted of 12 items and factor two six items. Reliability analysis for these two sets of items produced Cronbach’s alpha coefficients of .92 and .89 for factor one and two respectively. An inspection of the items that constituted each factor led to the conclusion that factor one is measuring Perceived risk to health from cannabis use, and factor two is measuring Reduction of cannabis use; both subscales were incorporated in all further analyses. See Table 1 for the 18 items and associated loadings on the two factors.

Table 1.

Health Messages Resistance Scale (for cannabis) items and associated loadings on the two factors

Scale item	Factor loading
Factor 1. Perceived risk to health from cannabis use
Cannabis contains many cancer causing agents	.76
Smoking cannabis can shorten your life	.73
Some psychological problems are made worse by the use of cannabis	.72
Long-term cannabis users have greater impairment in learning, memory and attention than non-users	.69
Cannabis smoke contains many harmful chemicals	.69
Smoking cannabis can lead to fatal diseases	.68
Breathing air which contains cannabis smoke can be bad for your health	.67
The longer the individual has been using cannabis the greater the likelihood of illness	.64
I believe the claims made by the cannabis literature which states the possible adverse side-effects	.64
People who smoke cannabis are more likely to develop coronary heart disease than non-cannabis smokers	.62
Heavy cannabis use can cause panic and paranoia through the disturbance of perception caused by the drug	.62
People who take cannabis are more likely to suffer from psychological problems than non-users	.62
Factor 2. Reduction of cannabis use
The Government should be more active in discouraging the smoking of cannabis	.84
There needs to be more advertisements trying to persuade people not to smoke cannabis	.84
A lot more needs to be done to prevent people from starting to use cannabis	.75
Penalties for first-time possession of cannabis should be more severe	.74
Breathing other people’s smoke does not worry me	.68
Advertisements trying to persuade me not to use cannabis would make me more aware of the dangers of cannabis smoking	.64

Bivariate correlational analyses

In order to investigate associations between the predictor variables and resistance to the health education material initial bivariate Pearson’s correlations were performed (see Table 2). Fifteen variables correlated significantly with the dependent variable, perceived risk to health of cannabis use and 16 with the dependent variable, reducing cannabis use.

Table 2.

Bivariate correlations between the independent variables and the two subscales of the Health Messages Resistance Scale (for cannabis)

	Acceptance and rejection scale
	Perceived risk to health from cannabis use		Reducing cannabis use
	r	p	b	r
Frequency of cannabis use	.41	<.001	.54	<.001
Frequency of other drug use	−.18	.03	−.37	<.001
Sex	.10	.16	.31	<.001
Ethnicity	.01	.48	.38	<.001
Health value	−.26	.003	−.34	<.001
Proactive negativism (NDS)	.25	.004	.34	<.001
Telic (MSP)	−.26	.003	−.37	<.001
Arousal avoidance (MSP)	−.33	<.001	−.37	<.001
Proactive negativism (MSP)	.28	.002	.41	<.001
Conformity (MSP)	−.27	.002	−.29	.001
Autic mastery (MSP)	−.17	.04	−.03	.39
Alloic sympathy (MSP)	−.17	.04	−.28	.001
Effortfulness (MSP)	−.18	.03	−.34	<.001
Telic dominance (MSP)	−.21	.01	−.36	<.001
Arousal avoidance dominance (MSP)	−.20	.02	.27	.002
Proactive negativism dominance (MSP)	.33	<.001	.44	<.001

Note: MSP – paratelic, MSP – arousal seeking, MSP – autic sympathy, MSP – alloic mastery, MSP – optimism, MSP – pessimism, MSP – arousability, MSP – reactive negativism, MSP – alloic mastery, MSP – reactive negativism dominance, MSP – mastery dominance, MSP – autic dominance, MSP – optimism dominance, NDS – reactive negativism dominance and age were not significantly associated with resistance and are not included in the table.

Multivariate correlational analyses

Following from this, standard multiple regressions were performed for each of the statistically significant variables from the bivariate correlations and the two subscales of the Health Messages Resistance Scale (for Cannabis).

Due to the large number of independent variables (i.e. 15 and 16), two initial regressions were computed for the Health Messages Resistance Scale (for Cannabis) subscale ‘Perceived risk to health from cannabis use’. As after Tabachnick and Fidell (2007), a maximum of eight independent variables could be chosen for inclusion in these preliminary regressions (as determined by establishing ‘N’ in the formula: 50 + (8xN) ≤ number of participants). The significant variables from these regressions were then entered into a third regression. The same procedure was carried out on the second Health Messages Resistance Scale (for Cannabis) subscale of ‘Reducing cannabis use’, with three initial regressions from which the significant variables were selected for the final regression. The results of the regressions are shown in Table 3.

Table 3.

Multiple regression analysis on the two Health Messages Resistance (for cannabis) subscales: Perceived risk to health from cannabis use and Reducing cannabis use

	Adjusted R²	F	p	beta	t	p
Message Resistance – Perceived risk to health from cannabis use
Frequency of cannabis use	.27	11.44	<.001	.39	4.55	<.001
Autic mastery (MSP)				−.28	−2.99	.003
Proactive negativism (MSP)				.22	2.35	.020
Health value				−.12	−1.36	.18
Messages Resistance – Reducing cannabis use
Frequency of cannabis use	.48	20.95	<.001	.41	5.52	<.001
Health value				−.26	−3.64	<.001
Ethnicity				.25	−3.45	.001
Proactive negativism dominance (MSP)				.27	1.91	.06
Proactive negativism				−.05	.38	.71

Both multiple regressions had F values that were statistically significant (p =< .001). The adjusted R² for ‘Reducing cannabis use’ was .27 (27% of the variance in this form of resistance is accounted for by the predictor variables). For the ‘Perceived risk to health from cannabis’ subscale the adjusted R² was .48, that is 48% of the variance in this form of resistance is accounted for by the predictor variables. An inspection of the individual contributions of the predictor variables shows that the frequency of cannabis use is the strongest predictor of resistance for both subscales. In consideration of the psychological variables investigated, autic mastery and proactive negativism as measured by the MSP were associated with Perceived risk to health from cannabis use, with beta weight scores of .28 and .22 respectively; and, for Reducing cannabis use, health value and ethnicity were significantly associated with this form of resistance, with beta weight scores of .26 and .25 respectively. Proactive negativism dominance was approaching significance on the latter dependent variable.

Power calculation

A calculation was performed using ‘G*Power 3.1’ (Faul et al., 2009) with the type of test specified as multiple regression. Employing a medium effect size (.15), a significance level of p = .05, with five predictor variables (the greatest number of predictor variables used in one single regression) the power achieved was 89%. Therefore this study had sufficient power to detect an effect if one existed.

Discussion

The study presented here investigated the factors implicated in resistance to health education messages about the effects of cannabis use. The independent variable with the greatest predictive utility for resistance to the health education messages was frequency of cannabis use. This was so for both resistance subscales, namely Resistance to messages about the perceived risks to health of cannabis use and Reducing cannabis use. The most frequent cannabis users tended to score more highly on these two subscales than infrequent users. However, as frequency of cannabis use is not a psychological construct, the utility of such a finding is limited in terms of its implications for behavioural change programmes.

Of the psychological states that were investigated, it was found that autic mastery and proactive negativism (as measured by the MSP) were independent of other variables and significantly predictive of resistance to messages about the Perceived risks to health from cannabis use. Also, it was found that health value and ethnicity have independent predictive effects for resistance to messages about Reducing cannabis use, with the proactive negativistic state (on the MSP) approaching significance (p < .06). That is, those who do not want personal power and control (autic mastery), those who feel compelled to rebel in order to obtain fun and excitement and those who do not value their health and who self-designate ethnically as ‘white’ tend to be more resistant to health education messages about the use of cannabis.

These findings provide support for the use of selected RT constructs in conjunction with other health-related and demographic variables for the prediction of resistance to health education messages, thereby partially replicating the findings of Lanarch and Brown (1997) in relation to user-status and rebelliousness scores.

In consideration of the wider RT literature on health behaviours, the findings here provide support for autic mastery and proactive rebelliousness as useful predictors.

Various previous studies (Hammil-Luker et al., 2004, Spijkerman et al., 2004) have implicated rebelliousness as a predictor of illicit drug use in the past, however have not specified a type of such non-conformity. It is salient to note here that the reactive form of rebelliousness, that which is about disaffection with the perceived requirements of significant others, was not found to be predictive. This is somewhat counterintuitive since previous studies have highlighted the importance of personality traits that reflect a lack of social bonding (Spooner, 1999) as predictive of drug abuse, of which reactive rebellion can be regarded as one such trait. So, it would appear that the proactive form of rebellion is important as a predictor through its relation to low control motivation, or as RT calls it, autic mastery.

Although not a psychologically modifiable variable, ethnicity was also found to be predictive of resistance to health education messages for cannabis use, with individuals who self-designate as white expressing more resistance. It is possible that being of white ethnic origin is a factor in the resistance to health education messages by way of frequency of cannabis use, with white ethnicity being previously shown to be associated with more frequent cannabis use than for those from a non-white population (Flatley, 2010). The identification of ethnic differences in reactance has also previously been observed by Seemann et al. (2004).

The measure of resistance to health education messages developed here, namely the Health Messages Resistance Scale (for Cannabis), is a progression from the scale developed by Crossley (2001a, 2002b, 2002a, 2002b) and has been shown to have good reliability. This measure is specific to cannabis use, while Crossley’s measure was generic. Also, an initial and substantively larger item pool was generated here to capture the potentially multidimensional nature of the resistance concept, albeit these items revealing in factor analysis only two distinct subscales. However, starting with an initial nine conceptual subscales has permitted the production of a simpler two-factor empirically derived model of resistance which thereby is likely to have a high degree of ecological validity.

Methodological limitations of the study include the use of a university student sample, so generalizing the findings to other populations needs to be undertaken cautiously, if at all. Indeed, Crossley (2002b), found student samples to be more resistant than random samples and that resistance varied between university faculties, with dental/health faculties showing the lowest resistance.

The study presented here has demonstrated that there are a number of factors important to resistance to health education messages, which consequently has implications for future health promotion campaigns. The demographic variables (frequency of cannabis use and having white ethnicity) can be used to target interventions for the most at risk populations. The psychological variables as identified (autic mastery, proactive negativism and health value) suggest ways for tailoring health education material to avoid triggering resistance to the health education messages. Specifically, health education messages should consider being:

non-didactic, designed not to invoke rebellion, by emphasizing choice and control (autic mastery), with messages taking account of young people’s need for excitement, and for sensation seeking through oppositional activity, as also suggested by Apter (2007);

short and simple, since long health promotions may trigger greater resistance (Dowd et al., 1991), with proactive negativism and autic mastery states being more likely to be triggered by long and detailed health promotions;

presented in a manner that does not equate the health behaviour with being ‘morally good’, and with humorous, paradoxical, playfully negativistic messages being composed for proactively rebellious resistant individuals.

Encouraging the modification of health behaviours continues to be a challenge for health professionals, with individuals retaining the right to chose unhealthy behaviours. However, the development of effective health education campaigns, based on a clear understanding of what predicts resistance to such messages, remains key for helping individuals make healthy behavioural choices.

Footnotes

Competing Interests

None declared.

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