Burdens in mental health recovery: Causal beliefs and their relation to stigma and help seeking recommendations

Abstract

Background:

Lay attitudes are often seen as potential barriers to mental health recovery. But apart from perceiving them as potential barriers, they can also play an important role in stimulating individuals to consult (in)formal help sources, in particular through the process of help referral. Where existing research mainly focusses on actual help seeking behaviour, this study will focus on lay publics’ referral behaviour.

Aims:

This study analyses the relation between causal beliefs and stigmatising attitudes and social distance on the one hand, and (in)formal help seeking recommendations on the other.

Methods:

Data from a survey carried out in 2019, assessing the attitudes of Public Health Insurance Provider (HIP) members towards mental health problems, was used. Part of the survey questions was based on a quantitative quasi-experimental design, using hypothetical written vignettes. The weighted data represent a sample of the adult Flemish population (22–94 years, N = 5675). Multiple linear regression analysis was used to examine the relation between causal beliefs and stigma, while binominal logistic regression analysis was used to investigate the relation between causal beliefs and help seeking recommendations.

Results:

The results reveal that causal beliefs significantly associate with stigma, measured by stigmatising attitudes and social distance: personal- or biogenetic beliefs associate with more stigma, where psychosocial beliefs associate with less stigma. Concerning help seeking recommendations, psychosocial beliefs associate with recommending psychological or psychotherapeutic care, biogenetic beliefs associate with advising help from general practitioners and suggesting psychiatric help, while personal beliefs negatively associate with recommending formal help. Meanwhile, biogenetic beliefs associate with less informal help seeking recommendations (e.g. family, friends and online self-help), where personal beliefs associate with more informal help seeking recommendations and psychosocial beliefs only associate with online help referral.

Conclusion:

This study highlights the importance of causal beliefs in affecting public stigma and help seeking recommendations.

Keywords

Public opinion survey mental health problems causal beliefs public stigma help seeking recommendations

Introduction

Lay attitudes are seen as potential barriers to mental health recovery (Schomerus & Angermeyer, 2008). For instance, the stigmatising attitudes prevalent in society may influence the help seeking behaviour of individuals with mental health problems. Existing studies show that the perceived public stigma around mental health problems associates with more negative attitudes towards help seeking (Wrigley et al., 2005), through mechanisms such as negative self-esteem (Corrigan et al., 2006). But apart from perceiving lay attitudes as potential barriers, they can also play an important role in stimulating individuals with mental health problems to consult formal or informal help sources, in particular through the process of help referral. Stimulating help seeking is important since delaying or avoiding formal help is associated with worse outcomes in various forms of mental distress (Boonstra et al., 2012; Dell’Osso et al., 2013). Encouraging informal help seeking is crucial as well, since it often precedes formal help seeking (Pattyn et al., 2013), but also because the lay public rates the helpfulness of informal help sources more highly than that of professionals (Crowe et al., 2014).

However, when analysing the attitudes and referral behaviour of the lay public, it is important to take into account their beliefs about the causation of mental distress (Angermeyer et al., 1999; Moro et al., 2015). Causal beliefs may play an important role in the attitudes of the lay public towards individuals with mental health problems, and also to which help sources they refer those individuals. Moreover, where previous research mainly focused on how causal beliefs influence the actual help seeking behaviour of individuals, this study focused on the lay public and analysed how causal beliefs not only influence their levels of public stigma, but also their referral behaviour. Analysing the lay publics’ referral behaviour is important since their attitudes on help seeking may influence the actual help seeking behaviour of individuals with mental distress (Angermeyer et al., 2001; Angermeyer & Matschinger, 2003). Besides, help referral measured by the propensity to recommend certain forms of help, indicates a correct time ordering. Help seeking behaviour on contrary, is mainly measured retrospectively, while causal beliefs and stigma are generally measured at the moment of the survey, thus hampering strictly correct interpretations.

The goal of this study was thus twofold; First we analysed the relationship between causal beliefs and stigmatising attitudes and social distance. Second, we analysed the relationship between causal beliefs and help seeking recommendations.

This study focused on the Flemish population (region in Belgium), which is especially meaningful since help seeking recommendations are not limited by crucial structural barriers (Pattyn et al., 2013). There is sufficient supply of (mental) health professionals and access to specialised mental health care is unrestricted (Aga et al., 2017; Schokkaert, 2016). In addition, we also focused on psychosis and depression, two common forms of mental distress in Flanders (Aga et al., 2017).

Causal beliefs and public stigma

Etiological beliefs of mental health problems have already been extensively studied. Recurrently indicated types of causal beliefs are personal-, biogenetic- and psychosocial beliefs. Personal beliefs stress individual characteristic explanations (Weiner, 1985, 1995). Previous research has already demonstrated that personal beliefs induce stigma (Corrigan et al., 2002; Martin et al., 2000). According to Weiner (1995), adhering to a personal model for mental distress leads to anger because of the belief that the person is responsible for his or her situation, which in turn induces stigmatising- and social distancing behaviour.

As a reaction on the personal model, and as a result of hegemonic biological psychiatry (Deacon, 2013), the ‘mental illness is an illness like any other’ rhetoric was adopted, encouraging biogenetic beliefs who explain mental distress as caused by brain disfunctioning (Albee & Joffe, 2004; Pescosolido et al., 2010). The aim of the rhetoric was to remove the blame and anger attached to mental disorders by diminishing personal responsibility (Albee & Joffe, 2004; Conrad, 1992). However, the belief that biogenetic explanations initiate more positive attitudes has been contested (Phelan, 2005). In fact, previous research has shown that biogenetic models indeed reduce blame, but on the other hand provoke stigma (Mehta & Farina, 1997), as it depicts the mentally disordered as fundamentally different, and the disorder is seen as persistent and serious (Kvaale et al., 2013). These perceptions in their turn, leverage unfavourable attitudes and behavioural actions (Phelan, 2005).

While several researches have concentrated on the consequences of the biomedical model on stigma, less attention has been paid to the relation between psychosocial beliefs and stigma (Haslam, 2005). According to the latter, mental health problems need to be considered as a life crisis, caused by environmental stressors (Read et al., 2006). Contrary to biogenetic causes, psychosocial causes seem to be linked with less stigmatising- attitudes and behaviour (Lam et al., 2005; Martin et al., 2000; Mehta & Farina, 1997), this because the individual is not considered responsible and thus relieved of blame, without perceiving them as fundamentally different (Walker & Read, 2002).

Causal beliefs and help seeking recommendations

Lay publics’ help seeking recommendations are likely to be guided by causal beliefs (Angermeyer et al., 2013). To date, research has mainly considered the relation between causal beliefs and actual help seeking behaviours, not focusing on help recommendations proposed by the lay public. These studies for instance propose that biogenetic beliefs facilitate more professional help seeking (e.g. Angermeyer & Dietrich, 2006; Pattyn et al., 2013; Schnittker, 2013). This presumption is also confirmed by a meta-analysis that found an increase in both biogenetic illness conceptualisations and the popularity of medical care, including psychotropic medication (Schomerus & Angermeyer, 2008). Kuppin and Carpiano (2006) nuanced these findings and argued that biogenetic beliefs specifically associate with more strictly psychiatric help seeking.

Psychosocial beliefs by contrast, seem to be correlated to more informal help seeking (Pattyn et al., 2013). Existing research shows that psychosocial beliefs are linked to preferring complementary and alternative mental health care treatments, such as homeopathy and naturopathy (Lauber et al., 2005). These studies have predominately a negative undertone, claiming that informal help or alternative health care is not appropriate in case of severe mental health complaints (Angermeyer et al., 1999).

Extending previous studies, this research will focus on the relationship between causal beliefs and lay publics’ help seeking recommendations, as this may influence the actual help seeking behaviour of individuals with mental distress (Angermeyer & Matschinger, 2003). Although Ajzen (1991) theory of planned behaviour has been subject to criticism, some elements of the theory may serve as a theoretical background to determine the importance of lay publics’ help seeking recommendations on actual help seeking behaviours. The theory states that behaviour is in part determined by subjective norms, which are composed of normative expectations (Ajzen, 1991; Angermeyer et al., 2001). Normative expectations in their turn, are oriented to ideas currently prevalent in society, thus strongly being influenced by lay publics’ ideas of possible sources of help for mental distress.

When analysing help seeking recommendations, both formal and informal recommendations need to be considered (Pattyn et al., 2013). Existing research has mainly focused on formal help for mental health problems, but informal help is equally important. The ‘overlapping waves of action’ theory illustrates that people have often already pursued informal help seeking before consulting formal help (Jorm et al., 2004). Moreover, Pescosolido’s (1992) pioneering work on ‘social organization strategy’ showed that help seeking decisions must be seen as an ongoing series of steps. It stated that individuals make help seeking decisions in interaction with others, by stressing the importance of social network structures in the help seeking process, where individuals consider an entire range of options when they respond to mental health complaints.

Psychosis and depression

The influence of causal beliefs on public stigma and help seeking recommendations can be expected to vary according to mental health problem. For example, Angermeyer and Matschinger (2003) showed that different mental health problems evoke different emotional reactions (e.g. fear, anger, pity) and personal attributes (e.g. dangerousness, dependency), which may in turn influence stigmatising attitudes and social distancing behaviour. In addition, various studies show that help seeking behaviour differs across mental health problems (e.g. Kvaale et al., 2013; Lauber et al., 2001; Riedel-Heller et al., 2005), leading us to assume that help seeking recommendations may also differ across mental health problems. This study will therefore investigate the associations between causal beliefs and the dependent variables separately for psychosis and depression.

When addressing public stigma and help seeking recommendations, some potential predictor variables should be controlled for; First, women are more inclined to endorse psychosocial beliefs (Pattyn et al., 2013). Second, people with higher educations are less likely to stigmatise (Corrigan & Watson, 2007) and more likely to contact specialised care (ten Have et al., 2003). Further, older people seem to report more stigma (Lauber et al., 2005) and are more likely to rely on general care (Cole & Yaffe, 1996). Also, the consideration whether people themselves have mental health complaints is crucial, as this might influence their stigma levels (Alexander & Link, 2003) and help seeking recommendations (Lauber et al., 2005).

Methodology

Sample and data

Data were derived from the ‘How are you doing’ (2019) survey, carried out in Flanders (Belgium) by the Christian Mutuality (CM), a Belgian Public Health Insurance Provider (HIP). In Belgium, public healthcare is accessed through HIPs. Everyone who lives and/or works in Belgium is obliged to join a HIP (Nonneman & Van Doorslaer, 1994). In exchange for paying social security contributions, access to subsidized public healthcare is provided. With a membership rate of 52.21% of Flemish HIP-members, the CM is the largest HIP in Flanders, followed by the Socialist Mutuality (22.46%) and the group of ‘Mutualités Libres’ (14.26%) (RIZIV, 2019).

The survey examined the attitudes of the HIP-members towards people with mental health problems and their help seeking recommendations. Part of the survey consisted of questions referring to hypothetical vignettes. Non-labelled written vignettes depicting a fictive situation were developed (see Appendix A). Four vignettes were used and randomly allocated: a man with depressive symptoms, a man with psychotic symptoms, a woman with depressive symptoms and a woman with psychotic symptoms. The other survey questions gauged own experiences with mental health complaints.

Given that four vignettes were designed, four random samples were drawn from the members file of the CM from whom they had an email address and from whom they had been authorized to use this email address for online surveys. This resulted in four samples of 25,000 members. In total, 7,034 out of 1,00,000 respondents answered the introductory questions (response rate=7.03%). To define the final sample, respondents who did not answer any questions after the introductory questions were excluded for further analysis (N = 1331). In addition, 28 respondents were removed from the sample due to missing information on gender or geographical location. After cleaning, 5,675 respondents (aged between 22 and 94 years) were included in this study. To address the high unit non-response, the sample was weighted on age, gender and geographical location (see Appendix B).

Variables

Since gender differences were not the main focus of this study, we combined the vignettes used in the survey in two categories (0 = psychosis, 1 = depression). Analyses were run separately for each vignette group.

Dependent variables

To assess stigmatising attitudes, a 6-item scale was used (see Appendix C). The items were derived from ‘The Stigma in Global Context – Mental Health Study’ (SGC-MHS) questionnaire (Pescosolido et al., 2013). Responses were recorded on a 5-point Likert scale ranging from ‘completely agree’ to ‘completely disagree’ (Cronbach’s $α$ = 0.68). Higher scores indicate more stigmatising attitudes.

For the assessment of social distance, a 4-item scale was used, presenting the following social relationships/activities to the respondent: neighbour, spending free-time, friendship and close work relationship. These items are as well derived from the SGC-MHS questionnaire and are measured on a 5-point Likert scale ranging from ‘absolutely not willing’ to ‘absolutely willing’ (Cronbach’s $α$ = 0.86). Higher scores indicate a greater desire for social distance.

Help seeking recommendations were assessed by the survey question: ‘to whom would you refer the vignette person?’ In total 16 possible help seeking recommendations were given. These were clustered in eight categories: general practitioner, psychologist and psychotherapist, psychiatrist, social worker, complementary therapy, family, friends and acquaintances and online self-help. Respondents received a score of 1 if they indicated (an) option(s) clustered in a category.

Independent variables

Causal beliefs were assessed by responses to nine items, also relying on the SGC-MHS questionnaire. Based on a factor analysis (rotation: Varimax), three categories of causal beliefs were discerned: two items reflect a biogenetic causal belief (brain disease, heredity) (Spearman-Brown = 0.55), three items reflect a personal causal belief (personal character, lack of will power, due to parenting style) (Cronbach’s $α$ = 0.62) and four items reflect a psychosocial causal belief (stress, life event, financial problems, high demands of society) (Cronbach’s $α$ =0.67). Using a 5-point Likert scale ranging from ‘definitely no cause’ to ‘definitely a cause’, respondents had to indicate how relevant they considered each potential cause.

Control variables

The control variables included are: gender (1 = women), marital status (1 = unmarried or single [ref.cat], 2 = married or cohabiting, 3 = separated, divorced or widowed), education (1 = secondary education or lower [ref.cat], 2 = non-university higher education, 3 = university education or doctorate), currently or formerly mental health complaints (1 = no), age (measured in years) and mental health status (Cronbach’s $α$ = 0.87), measured in accordance with the CES-D8 scale (Radloff, 1977; Van de Velde et al., 2009).

Analysis

After running univariate descriptive statistics (as illustrated in Table 1), different models were tested for depressive and psychotic symptoms separately. First, we tested the association between causal beliefs and stigmatising attitudes and social distance, using multiple linear regression analysis. The unstandardized coefficients, the 95% confidence intervals and the p values are reported in Table 2. Second, the association between causal beliefs and help seeking recommendations was tested, using binominal logistic regression models. The odds ratios, their 95% confidence intervals and the corresponding p values are reported in Tables 3 and 4. All analyses have been performed in the statistical program SPSS 26.

Table 1.

Univariate statistics of study population and (in)dependent variables by vignette (N = 5675, How are you doing? 2019, CM).

Study population	N		%
Gender
Men	2830		49.9
Women	2845		50.1
Marital status
Unmarried or single	1045		18.4
Married or cohabiting	3880		68.4
Divorced, separated or widowed	748		68.4
Education
Secondary education or lower	2313		40.7
Non-university higher education	1885		33.2
University education or doctorate	1405		24.8
Mental health complaints - currently or past
Yes	2732		48.1
No	2863		50.5
	Mean (SD)		Min.–max.
Age	50.11(15.35)		22–94
Mental health status	13.26 (4.25)		7–28
Dependent variables	Psychosis vignette (N = 2,747, 48.4%)		Depression vignette (N = 2,928, 51.6%)
Dependent variables	Mean (SD)	Min.-max.	Mean (SD)	Min.–max.
Stigmatising attitudes	16.61 (3.47)	6–30	14.38 (3.25)	6–30
Social distance	9.66 (3.11)	4–20	8.81 (2.98)	4–20
Help seeking recommendations	N	%	N	%
Formal help seeking recommendations
General practitioner	2203	80.2	2366	80.8
Psychologist or psychotherapist	1914	69.7	2118	72.3
Psychiatrist	1190	43.3	770	26.3
Social worker	478	17.4	523	17.9
Complementary therapy	194	7.1	258	8.8
Informal help-seeking recommendations
Family	1007	36.7	1238	42.3
Friends or acquaintances	761	27.7	1132	38.6
Online self-help	360	31.1	392	13.4
Independent variables	Mean (SD)	Min.–max.	Mean (SD)	Min. –max.
Causal beliefs
Biogenetic causal beliefs	5.69 (1.77)	2–10	4.82 (1.57)	2–10
Personal causal beliefs	7.18 (2.40)	3–15	6.89 (2.34)	3–15
Psychosocial causal beliefs	15.14 (2.49)	4–20	15.67 (2.31)	4–20

Table 2.

The association between causal beliefs and public stigma components (N = 5675, How are you doing? 2019, CM).

	Psychosis vignette				Depression vignette
	Stigmatising attitudes		Social distance		Stigmatising attitudes		Social distance
	B	(95 % CIs)	B	(95 % CIs)	B	(95 % CIs)	B	(95 % CIs)
Biogenetic causal beliefs	0.46	(0.38 to 0.53)***	0.15	(0.08 to 0.22)***	0.21	(0.13 to 0.28)***	0.06	(−0.01 to 0.14)
Personal causal beliefs	0.16	(0.11 to 0.22)***	0.13	(0.08 to 0.18)***	0.24	(0.19 to 0.29)***	0.24	(0.19 to 0.29)***
Psychosocial causal beliefs	−0.12	(−0.17 to −0.07)***	−0.09	(−0.14 to −0.04)***	−0.01	(−0.06 to 0.05)	−0.10	(−0.15 to −0.06) ***
Mental health complaints – currently or past (ref.cat.: yes)	0.87	(0.60 to 1.16)***	0.81	(0.55 to 1.08)***	0.81	(0.55 to 1.07) ***	0.61	(0.37 to 0.85) ***
Mental health status	0.07	(0.03 to 0.10) ***	−0.01	(−0.04 to 0.03)	0.61	(0.03 to 0.09) ***	−0.03	(−0.06 to 0.00)
Women	−0.25	(−0.51 to 0.01)	−0.12	(−0.46 to 0.02)	0.84	(−1.07 to −0.61)***	0.11	(−0.11 to 0.33)
Age	0.03	(0.02 to 0.04)***	0.01	(−0.00 to 0.02)	0.02	(0.01 to 0.03)***	0.20	(0.01 to 0.03)***
Education (ref.cat.: secondary education or lower)
Non-university higher education	−0.12	(−0.41 to 0.18)	−0.46	(−0.73 to −0.19)**	−0.31	(−0.57 to −0.04)*	0.17	(−0.08 to 0.42)
University education or doctorate	0.32	(−0.01 to 0.66)	−0.21	(−0.52 to 0.10)	−0.51	(−0.82 to −0.21)**	0.10	(0.19 to 0.38)
Marital status (ref.cat.: unmarried or single)
Married or cohabiting	−0.01	(−0.35 to 0.34)	−0.20	(−0.52 to 0.12)	−0.19	(−0.50 to 0.12)	−0.47	(−0.76 to −0.18)**
Divorced, separated or widowed	0.11	(−0.39 to 0.60)	0.02	(−0.44 to 0.48)	−0.33	(−0.76 to 0.10)	−0.23	(−0.63 to 0.17)

Note. *p < .05. **p < .01. ***p < .001.

Table 3.

The association between causal beliefs and formal help seeking recommendations (N=5675, How are you doing? 2019, CM).

Psychosis vignette
	General practitioner		Psychologist, psychotherapist		Psychiatrist		Social worker		Complementary therapy
	OR	(95% CIs)	OR	(95% CIs)	OR	(95% CIs)	OR	(95% CIs)	OR	(95% CIs)
Biogenetic causal beliefs	1.10	(1.04–1.17)**	0.95	(0.90–1.00)*	1.16	(1.11–1.22)***	1.08	(1.02–1.15)*	0.98	(0.90–1.07)
Personal causal beliefs	0.94	(0.90–0.98)**	0.97	(0.93–1.00)*	0.88	(0.85–0.91)***	1.00	(0.96–1.05)	1.12	(1.03–1.18)**
Psychosocial causal beliefs	1.03	(0.99–1.07)	1.09	(1.05–1.13)***	1.00	(0.97–1.04)	1.15	(1.10–1.20)***	1.10	(1.03–1.18)**
Mental health complaints – currently or past (ref.cat.: yes)	1.07	(0.85–1.33)	0.82	(0.67–1.00)	0.85	(0.71–1.02)	1.54	(1.21–1.97)***	0.65	(0.45–0.92)*
Mental health status	0.95	(0.92–0.97)***	1.02	(0.99–1.04)	1.04	(1.02–1.06)**	0.99	(0.96–1.02)	1.03	(0.99–1.07)
Women	1.45	(1.18–1.77)***	1.77	(1.48–2.12)***	1.18	(1.00–1.39)	1.24	(1.00–1.53)	2.01	(1.45–2.80)***
Age	1.00	(0.99–1.00)	0.98	(0.97–0.98)***	1.00	(0.99–1.00)	1.01	(1.00–1.02)**	0.98	(0.97–1.00)**
Education (ref.cat.: secondary education or lower)
Non-university higher education	1.30	(1.04–1.63)*	1.20	(0.98–1.47)	1.22	(1.01–1.47)*	0.72	(0.57–0.91)**	1.30	(0.92–1.85)
University education or doctorate	1.36	(1.05–1.77)*	1.77	(1.39–2.24)***	1.65	(1.33–2.04)***	0.51	(0.38–0.69)***	0.92	(0.59–1.42)
Marital status (ref.cat.: unmarried or single)
Married or cohabiting	0.87	(0.66–1.14)	1.25	(0.97–1.59)	0.95	(0.76–1.19)	0.77	(0.57–1.02)	0.82	(0.56–1.21)
Divorced, separated or widowed	0.76	(0.52–1.11)	1.34	(0.95–1.88)	0.88	(0.64–1.20)	0.96	(0.65–1.42)	0.92	(0.51–1.65)
Stigmatising attitudes	1.01	(0.98–1.05)	1.00	(0.97–1.03)	1.07	(1.04–1.10)***	1.00	(0.96–1.04)*	0.94	(0.89–0.99)*
Social distance	1.00	(0.96–1.03)	0.96	(0.93–0.99)*	0.99	(0.96–1.02)	0.96	(0.92–1.00)	1.05	(1.00–1.11)
Depression vignette
	OR	(95 % CIs)	OR	(95 % CIs)	OR	(95 % CIs)	OR	(95 % CIs)	OR	(95 % CIs)
Biogenetic attribution	1.07	(1.01–1.15) *	0.99	(0.94–1.05)	1.16	(1.09–1.22) ***	1.04	(0.97–1.11)	1.03	(0.94–1.13)
Personal attribution	0.92	(0.88–0.96)***	0.94	(0.90–0.98)**	0.91	(0.88–0.95)***	1.00	(0.95–1.04)	1.12	(1.06–1.20)***
Psychosocial attribution	1.00	(0.95–1.04)	1.09	(1.05–1.13) ***	1.00	(0.96–1.04)	1.06	(1.01–1.11) *	1.10	(1.03–1.17) **
Mental health complaints – currently or past (ref.cat.: yes)	1.01	(0.81–1.26)	0.79	(0.65–0.97)*	0.87	(0.72–106)	1.11	(0.88–1.39)	0.63	(0.46–0.86)**
Mental health status	0.98	(0.96–1.01)	1.00	(0.97–1.02)	1.03	(1.01–1.05)*	1.01	(0.98–1.04)	1.01	(0.98–1.04)
Women	1.30	(1.06–1.58)*	1.43	(1.19–1.71)***	0.90	(0.76–1.08)	0.89	(0.72–1.08)	3.26	(2.37–4.49) ***
Age	1.00	(0.99–1.00)	0.98	(0.97–0.98)***	1.01	(1.00–1.01)	1.02	(1.01–1.02)***	0.99	(0.98–1.00)*
Education (ref.cat.: secondary education or lower)
Non-university higher education	1.23	(0.98–1.55)	1.28	(1.05–1.57)*	1.16	(0.95–1.42)	0.62	(0.49–0.78)***	1.02	(0.75–1.40)
University education or doctorate	0.99	(0.77–1.28)	1.71	(1.34–2.16) ***	1.08	(0.86–1.37)	0.69	(0.53–0.91) **	0.75	(0.51–1.09)
Marital status (ref.cat.: unmarried or single)
Married or cohabiting	1.46	(1.13–1.88)**	0.86	(0.68–1.13)	1.03	(0.82–1.31)	0.72	(0.55–0.94)*	0.67	(0.47–0.94)*
Divorced, separated or widowed	1.22	(0.86–1.73)	0.96	(0.69–1.35)	1.00	(0.73–1.39)	0.82	(0.57–1.17)	0.85	(0.53–1.36)
Stigmatising attitudes	0.99	(0.95–1.02)	1.00	(0.97–1.04)	1.06	(1.03–1.10) ***	1.04	(1.00–1.08) *	0.98	(0.93–1.03)
Social distance	1.02	(0.99–1.06)	0.95	(0.92–0.98)**	0.94	(0.91–0.97) ***	0.98	(0.94–1.01)	0.99	(0.94–1.05)

Note. *p < .05. **p < .01. ***p < .001.

Table 4.

The association between causal beliefs and informal help seeking recommendations (N=5675, How are you doing? 2019, CM).

Psychosis vignette
	Family		Friends, acquaintances		Online self-help
	OR	(95% CIs)	OR	(95% CIs)	OR	(95% CIs)
Biogenetic causal beliefs	0.94	(0.90–0.99)*	0.94	(0.90–0.99)*	1.05	(0.98–1.12)
Personal causal beliefs	1.06	(1.02–1.09)**	1.11	(1.07–1.15)**	1.01	(0.96–1.06)
Psychosocial causal beliefs	0.99	(0.95–10.02)	1.03	(0.99–1.07)	1.05	(0.99–1.09)
Mental health complaints – currently or past (ref.cat.: yes)	0.91	(0.76–1.10)	0.81	(0.69–1.03)	0.92	(0.70–1.19)
Mental health status	1.01	(0.99–1.03)	0.99	(0.96–1.01)	1.02	(0.99–1.05)
Women	0.95	(0.80–1.12)	0.97	(0.81–1.15)	1.33	(1.05–1.68)*
Age	0.98	(0.98–0.99)***	1.00	(0.99–1.00)	0.99	(0.98–1.00)**
Education (ref.cat.: secondary education or lower)
Non-university higher education	0.91	(0.75–1.10)	0.98	(0.80–1.21)	0.84	(0.65–1.10)
University education or doctorate	1.16	(0.94–1.44)	1.29	1.03–1.62)*	0.67	(0.49–0.92)*
Marital status (ref.cat.: unmarried or single)
Married or cohabiting	1.24	(1.00–1.55)	0.98	(0.77–1.24)	1.10	(0.80–1.15)
Divorced, separated or widowed	1.00	(0.72–1.39)	1.03	(0.73–1.46)	0.83	(0.52–1.34)
Stigmatising attitudes	0.99	(0.96–1.02)	0.99	(0.96–1.02)	1.04	(1.00–1.08)*
Social distance	0.97	(0.94–1.00)*	0.93	(0.90–0.96)***	0.95	(0.91–0.99)*
Depression vignette
	OR	(95% CIs)	OR	(95% CIs)	OR	(95% CIs)
Biogenetic causal beliefs	1.03	(0.97–1.08)	0.99	(0.93–1.03)	1.03	(0.96–1.11)
Personal causal beliefs	1.01	(0.97–1.05)	1.06	(1.02–1.10)**	1.00	(0.95–1.05)
Psychosocial causal beliefs	1.00	(0.97–1.04)	0.98	(0.95–1.01)	1.05	(1.00–1.11)*
Mental health complaints – currently or past (ref.cat.: yes)	1.14	(0.96–1.36)	0.96	(0.81–1.15)	0.84	(0.66–1.08)
Mental health status	1.01	(0.99–1.03)	0.98	(0.96–1.00)*	1.00	(0.97–1.03)
Women	0.83	(0.71–0.97)*	0.80	(0.68–0.94)**	1.00	(0.80–1.25)
Age	0.99	(0.98–0.99)***	0.99	(0.98–1.00)**	1.00	(0.99–1.01)
Education (ref.cat.: secondary education or lower)
Non-university higher education	1.25	(1.04–1.49)*	1.52	(1.27–1.83)***	1.23	(0.95–1.60)
University education or doctorate	1.49	(1.21–1.83)***	1.92	(1.56–2.37)***	1.25	(0.93–1.68)
Marital status (ref.cat.: unmarried or single)
Married or cohabiting	1.39	(1.13–1.72)**	0.85	(0.68–1.05)	0.64	(0.48–0.84)**
Divorced, separated or widowed	1.05	(0.78–1.41)	0.85	(0.63–1.15)	0.65	(0.43–0.97)*
Stigmatising attitudes	0.97	(0.94–0.99)*	0.94	(0.91–0.97)***	0.98	(0.94–1.02)
Social distance	0.98	(0.95–1.01)	0.97	(0.94–1.00)	1.01	(0.97–1.05)

Note. *p < .05. **p < .01. ***p < .001.

Results

The univariate statistics of the (in)dependent variables presented in Table 1 reveal that stigmatising attitudes ( $\bar{X}$ _psychosis:16.61, $\bar{X}$ _depression:14.38) and social distance ( $\bar{X}$ _psychosis:9.66, $\bar{X}$ _depression:8.81) are more pronounced towards psychosis than depression. Regarding help seeking recommendations, we observe that referring to psychiatrists is more often chosen in the psychosis vignette (psychosis:43.3%, depression: 26.3%), whereas referring to psychologists or psychotherapists is more popular in the depression vignette (psychosis:69.7%, depression:72.3%). Informal help referral is more prevalent in the depression vignette, except for online self-help which is more opt in the psychosis vignette (psychosis:31.1%, depression:13.4%). Concerning causal beliefs; biogenetic- ( $\bar{X}$ _psychosis:5.69, $\bar{X}$ _depression:4.82) and personal beliefs $\bar{X}$ _psychosis:7.18, $\bar{X}$ _depression:6.89) are more popular in the psychosis vignette, whereas psychosocial beliefs ( $\bar{X}$ _psychosis:15.14, $\bar{X}$ _depression:15.67) are more selected in the depression vignette.

Table 2 concentrates on the relationship between causal beliefs and public stigma. It shows that biogenetic-, personal- and psychosocial beliefs significantly associate with stigmatising attitudes and social distance in the psychosis vignette. This also applies to the depression vignette, expcect for the association between psychosocial beliefs and stigmatising attitudes and biogenetic beliefs and social distance. More specifically, it appears that both biogenetic- and personal beliefs lead to more stigmatising attitudes and social distance, whereas psychosocial beliefs lead to less stigmatising attitudes and social distance.

Tables 3 and 4 focus on the relationship between causal beliefs and (in)formal help seeking recommendations. Concerning formal help recommendations, the results of both vignettes are largely the same: psychosocial beliefs are associated with referral to psychologists or psychotherapists, social workers and complementary therapy. Biogenetic beliefs associate with advising help from general practitioners and psychiatrists, and disadvising help from psychologists or psychotherapists, but only in the psychosis vignette. Personal beliefs overall relate to less formal help advising, except for complementary therapy.

Regarding informal help recommendations, more associations are found significant in the psychosis vignette. We observe that biogenetic beliefs associate with less referral to family and friends, and personal beliefs with more referral to family and friends. Regarding the depression vignette, personal beliefs again associate with referral to friends, and psychosocial beliefs relate to more online self-help advising.

While not the main focus of our study, we observe that stigmatising attitudes relate to psychiatric care referral in both vignettes. Social distance on the other hand, relates to less formal help referral in general. Further, stigmatising attitudes and social distance both associate with less informal help referral, except for the positive association between stigmatising attitudes and online self-help.

As regards the control variables, female respondents tend to report less stigma and more intention to recommend formal help but less intention to recommend informal help. Older respondents show more stigma and overall less intention to refer to formal and informal help. Higher educated respondents report less stigma and are more likely to recommend help from psychologists or psychotherapists but less likely to recommend help from social workers. Further, they are more willing to recommend informal help. Regarding marital status, significant differences are found between single or unmarried respondents and cohabiting or married respondents, where the latter are more likely to refer to general practitioners but less likely to refer to social workers and complementary therapy. Married or cohabiting respondents are also more likely to refer to family but less likely to recommend online self-help. Further, respondents with low scores on mental health tend to have more stigmatising attitudes and are more inclined to refer to psychiatrists, but less willing to refer to general practitioners in the psychosis vignette. Respondents currently struggling or with a history of mental health complaints report less stigma and more referral to complementary therapy, and also suggest psychological or psychotherapeutic help in the depression vignette.

Discussion

This study investigated the influence of causal beliefs on public stigma and help seeking recommendations. The first important findings of this research regard the relation between causal beliefs and public stigma. Concerning this relation, no substantial differences were found between the psychosis and depression vignette. Overall, biogenetic- and personal beliefs were associated with more stigmatising attitudes and social distance, while psychosocial beliefs were associated with less stigmatising attitudes and social distance.

According to these results, we can support previous research presuming that biogenetic- or personal explanations trigger stigma conceptualizations (Corrigan et al., 2003), while psychosocial explanations activate more tolerant attitudes (Lam et al., 2005; Martin et al., 2000; Mehta & Farina, 1997). The outcome for psychosocial beliefs corresponds to the attribution theory, which states that attributing the situation to environmental stressors reduces personal blame and as such stigma (Weiner et al., 1988). In line with genetic essentialist thinking, we find that biogenetic beliefs lead to more stigma, since such beliefs cause individuals to consider mental distress as dangerous, despite the fact that they diminish blame (Haslam, 2000; Link & Phelan, 2001).

The second cluster of findings concerns the relation between causal beliefs and help seeking recommendations. Again, no considerable differences were found between the psychosis and depression vignette. The results revealed that biogenetic beliefs facilitate formal help referral but disregard informal help referral. Nonetheless, the recommended formal help options were the classical medical professionals. Psychosocial beliefs were also associated with formal help referral, but here the suggested options were less based on medical interventions and more on first line psychological or psychotherapeutic care and complementary therapy. Psychosocial beliefs were also positively associated with referral to online self-help. Last, personal beliefs were in general associated with less formal help referral but more informal help referral.

Our results confirm previous research suggesting that biogenetic beliefs endorse medical interventions (Angermeyer et al., 2017; Khalsa et al., 2011). This might be positive since severe mental health complaints ask for specialised treatment. Although, studies show that biogenetic beliefs are also linked to pessimism about the effectiveness of interventions, because the disorder is perceived uncontrollable due to the biogenetic base (Phelan et al., 2006). The finding that psychosocial beliefs induce psychological or psychotherapeutic care referral is also in line with existing research (Dunlop et al., 2012). This may be favourable since the extensive evidence showing that psychological treatments are effective and efficacious in remedying mental distress (Craighead & Craighead, 2001; Wampold, 2007).

According to Angermeyer et al. (1999), biogenetic beliefs associate with less informal help seeking behaviour, so the finding that they were also associated with less informal help referral could be expected. The same study proves that psychosocial beliefs incite informal help seeking (Angermeyer et al., 1999), though in our study almost no significant associations were found between psychosocial beliefs and informal help referral. The finding that personal beliefs were associated with informal help referral could not be seen as entirely positive since they were also associated with disregarding formal help and inducing stigma.

While initially not the main focus of our study, the last crucial findings regard the relation between public stigma and help seeking recommendations. Stigmatising attitudes were associated with psychiatric help referral and disregarding informal help, while social distance was associated with neglecting both formal and informal help. Thus, where stigmatising attitudes push people towards specialised medical care, social distance intends to isolate people with mental distress completely.

Before drawing attention to the implications of this study, we first acknowledge an important limitation; the low response rate of the survey (7.03%). The CM made use of an online survey to collect data. Online surveys have many advantages, but are also associated with low response rates (Manfreda et al., 2008). In addition, due to the anonymous character of the survey and data collecting policy rules of the CM, recontacting participants was restricted, hampering total design methods (Fan & Yan, 2010). However, we mediated the low response rate in different ways; First, since low response rates were mainly due to unit nonresponse, weighting the data on socio-demographics was crucial (Haunberger, 2011). Hence, we designed post-stratification weights after data cleaning, based on age, gender and geographical location.

Second, because personal interests influence participation (Groves et al., 1992; Zillmann et al., 2014), and the survey gauged questions about mental health, various control variables were included during the analyses to avoid overrepresentation of certain participants (e.g. currently or past experiences with mental health complaints). This because the attitudes of ‘patient populations’ may differ from those of the lay public (Carter et al., 2018). In addition, several sociodemographic variables were incorporated as controls (e.g. education, marital status), since research has previously proven their influence in survey participation (Zillmann et al., 2014). In that way, we were also able to isolate selection biases and statistical inferences.

Third, since online surveys are often accompanied by high item nonresponse rates (Zillmann et al., 2014), respondents who only completed the introductory questions were removed. This was done before post-stratification weights were calculated. However, fallout was detected after the initiatory questions, when respondents were asked to read the case vignette, leading us to assume that they broke off due to practical reasons (e.g. perception of duration and length survey). After that, item nonresponse was almost non-existent (<1.5%).

Fourth, we conducted a sensitivity analysis to assess the robustness of our findings. We replicated the analysis testing different coding schemes for variables containing multiple categories. The interpretation of the results remained the same.

Moreover, several indicators lead to assume that our analyses are valuable; First, the results regarding the association between causal beliefs and public stigma are in consonance with existing research. Second, though at present almost no research analysed the relation between causal beliefs and help referral intentions, studies did already analyse the relation between causal beliefs and help seeking behaviour, and again we observe that our results are in line with those studies.

We conclude by highlighting some specific implications. One implication is clear; bad character or personal weakness explanations need to be absolved explicitly when trying to combat stigma and lower barriers to mental health recovery. Further, we conclude that applying biogenetic beliefs is double-sided. The dominant biomedical rhetoric put forward by health professionals drives individuals to recommend specialised medical care, but by contrast also induces stigma and disregards informal help referral.

Forthcoming research may perhaps enhance the endorsement of complex biopsychosocial explanations. These explanations may avoid the countereffects of prototypical biogenetic- or psychosocial explanations by stressing biogenetic-, psychological- and social causes in understanding what makes individuals mentally vulnerable (Andersson & Harkness, 2018; Ghaemi, 2009). Moreover, we should not engulf in ‘biology is destiny’ thinking, also because purely biogenetic- or neurobiological explanations for mental distress are contested by research and scientists (Deacon & McKay, 2015; Pordeus, 2015). Mental health problems are too complex to pinpoint single explanations. Perhaps a useful lesson obtained from this study is to rework policy initiatives not around one specific belief configuration (e.g. ‘mental illnesses are illnesses like any other’) but by combining different configurations. Implementing combined explanations may also leverage academic knowledge since belief configurations will not be fragmented into singular units, but instead can be used as potential overreaching explanations.

Footnotes

Appendixes

Appendix A. Example vignette of Tom

The vignette of Tom, psychotic symptoms, was as follows: “Tom did pretty well until a year ago, but then everything changed. He often thought that the people around him were telling bad things about him and gossiping behind his back. Tom was convinced that people were spying on him and that they could hear what he was thinking. Tom lost his enthusiasm in his work and family, retired to the house, and usually spent time alone. Tom was so preoccupied with his thoughts that he skipped meals and neglected his hygiene. At night, when everyone was asleep, he was pacing around the house. Tom heard voices who told him what he should do or think. He has been in this situation for the last six months.” The vignettes used in the survey were non-labelled.

Appendix C. Scale to measure stigmatising attitudes

Statements used to assess stigmatising attitudes: (1) whatever Tom/Sara achieves, his/her chances will always be limited, (2) people like Tom/Sara are unpredictable, (3) people like Tom/Sara are difficult to talk to, (4) people like Tom/Sara are as productive like any other person, (5) people like Tom/Sara are as reliable as any other person, (6) when Tom/Sara is suitable for a job, then he/she must have the same changes as any other person to be hired for the job.

Conflict of interest

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.

ORCID iD

Lisa Colman

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