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Assessment of Public Stigma Towards People with Mental Health Problems

A peer-reviewed version of this preprint was published in:
Nursing Reports 2026, 16(4), 126. https://doi.org/10.3390/nursrep16040126

Submitted:

06 March 2026

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06 March 2026

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Abstract
Background/Objectives: The aim is to assess public stigma related to people with mental health problems and to observe the influence of sociodemographic factors on it. Methods: This observational, descriptive, and cross-sectional study was conducted with a sample of 404 participants who completed a self-administered online questionnaire that included sociodemographic variables and the Community Attitudes towards Mental Illness Scale (CAMI-S, Spanish version). Non-parametric tests, multiple linear regression, and statistical power analyses were applied. Results: The mean CAMI-S total score was 84.89 (SD = 11.122), indicating a generally positive attitude towards community integration. Statistically significant associations (p-value ≤ 0.05) were found between CAMI-S scores and variables such as gender, age, place of residence, educational level, mental health disorder, and close contact with someone with mental health disorders. The regression model revealed four significant predictors of lower stigma: identifying as female (β = 2.523; p = 0.037), having a medium or higher educational level (β = 5.061; p = 0.002), experiencing a mental health diagnosis (β = 4.535; p = 0.014), and a close contact (β = 4.183; p < 0.001). Conclusions: These findings underscore the need for targeted anti-stigma strategies and reinforce the role of nursing in promoting mental health inclusion.
Keywords: 
public stigma
;  
mental health problems
;  
socio-demographic factors
;  
community inte-gration
;  
social rejection
Subject: 
Public Health and Healthcare  -   Nursing

1. Introduction

The Diagnostic and Statistical Manual of Mental Disorders (DSM-V-TR) defines a "mental disorder" as a "syndrome characterized by a clinically significant disturbance in an individual's cognition, emotional regulation, or behaviour, reflecting a dysfunction in the psychological, biological, or developmental processes underlying mental functioning" [1]
The concept of “mental disorder” is closely associated with the term "stigma", a concept that comes from the Ancient Greece, as a reference to physical marks placed on individuals to indicate negative or unusual moral status. These marks, often visible, served as a warning sign that the bearer was considered dishonourable and should be avoided, especially in public spaces. In 1963, sociologist Erving Goffman reintroduced the concept into the social sciences, defining stigma as an attribute that discredits an individual and reduces them to a lesser status in the perception of others [2].
Building on this initial definition, Link and Phelan proposed a structural approach to stigma [3], which was later revised and simplified by Andersen, Varga, and Folker. This group identified four key elements in the construction of stigma: labelling, negative stereotypes, linguistic separation, and power asymmetry. In their revision, they omitted two components of the original definition due to redundancy: status loss and discrimination, and emotional reaction. This approach enables an understanding of stigma as a collective social construct affecting groups, though its consequences are experienced individually [4,5].
Regarding typology, the classification proposed by Pryor and Reeder (2011) remains a key reference to understanding the different forms of stigma: public stigma, self-stigma, associative stigma, and structural stigma. This framework helps to analyse how stigma operates in different contexts, and the following types of stigmas were identified [6]:
  • Public stigma: Refers to society’s negative attitudes and discriminatory behaviours toward people with mental health problems.
  • Self-stigma: Involves the internalization of those attitudes, where individuals adopt society’s negative perception of their own condition.
  • Structural stigma: Includes the policies, laws, and institutional practices that perpetuate inequality and restrict rights.
  • Associative stigma: Involves prejudice and discrimination toward people connected with someone who is stigmatized, such as relatives or friends.
This study focuses on public stigma, which comprises three interdependent dimensions:
  • Stereotypes (cognitive): Socially held beliefs about mental illness (e.g., danger, weakness, incompetence).
  • Prejudice (emotional): Negative emotional reactions toward the stigmatized group.
  • Discrimination (behavioural): Resulting actions, such as social distancing or unequal treatment [7,8].
This tripartite model has also been systematized in the Mental Illness Stigma Framework, which conceptualizes stigma as a dynamic process involving these three interconnected components [9].
Public stigma negatively impacts individuals, reducing self-esteem, increasing feelings of guilt, and leading to social isolation. It also limits opportunities in education, employment, and community participation. The fear of social rejection may become a barrier to seeking professional help [10,11]. Because of this, stigma is often described as a “second illness,” obstructing prevention, treatment, and recovery [12].
Therefore, this study aims to assess public stigma related to people with mental health problems. Specifically, we aim to observe whether sociodemographic characteristics, presenting a mental health problem, or having close contact with someone with a mental health problem influence the stigma expressed by participants.
Note: The term "mental disorder" is used in quotation marks in this study to emphasize that, although it is widely used in the scientific literature and in diagnostic manuals such as the DSM-V-TR and the International Classification of Diseases (ICD-11), it can function as a diagnostic label that reinforces associated stigma. Therefore, whenever possible, the term “mental health problem” will be used, with the aim of promoting a more respectful and person-centered approach [13].

2. Materials and Methods

2.1. Study Design, Participants, and Setting

The study follows an observational, descriptive, and cross-sectional design. It was conducted between September 2023 and April 2025. It was carried out among the general population in Spain, involving individuals who voluntarily participated after being informed of the research objectives.
The target population consisted of individuals who met the established inclusion criteria. The sample size was calculated assuming that the total number of adults living in Spain who met the inclusion criteria was unknown (N = ∞). A 95% confidence level (Z = 1.96), 5% precision (d = 0.05), and an expected proportion of 50% (p = 0.5) were used. Based on these parameters, the minimum sample size was 384 participants. A non-probabilistic convenience sampling method was used to collect the sample. Participants were recruited through social media platforms such as Instagram, WhatsApp, and Twitter. Ultimately, 404 individuals were collected throughout the sampling period.

2.2. Inclusion and Exclusion Criteria

Participants eligible for inclusion in the study were required to be over 18 years of age, reside in Spain, and be able to read and comprehend Spanish. Individuals who had obtained a university degree in nursing, medicine, or psychology were excluded from participation.

2.3. Study Variables

On the one hand, the following variables were defined as independent: the sociodemographic variables — gender, age, place of residence, marital status, children, educational level, employment status, having a “mental disorder”, and having a close contact with someone with a “mental disorder”. On the other hand, the dependent variables were the 20 items from the Spanish version of the CAMI-S scale (Community Attitudes towards Mental Illness, Swedish version), expressed by a 5-point Likert scale response format (1 = strongly disagree; 2 = somewhat disagree; 3 = neither agree nor disagree; 4 = somewhat agree; 5 = strongly agree). Finally, the total score of the CAMI-S scale was calculated by recoding the negatively phrased items and generating a quantitative variable, which was named “total”.

2.4. Measurement Instruments

In 1980, Taylor and Dear developed the CAMI scale (Community Attitudes towards the Mentally Ill) as a tool to predict and explain society’s perception of people with severe mental health problems [14]. This version includes 40 questions grouped into four factors: authoritarianism, benevolence, social restrictiveness, and community mental health ideology, with satisfactory validity and reliability results. Later, Högberg et al. removed items with poor correlation, reducing the scale to 20 items, known as CAMI-S. The total questionnaire score ranges from 20 to 100, where higher scores reflect more suitable attitudes toward integration. Three factors are also examined: integration and contact, social distance, and dangerousness and avoidance [15].
In this study, the Spanish adapted and validated version of the CAMI-S scale by Sastre-Rus et al. [15] was used to assess public stigma toward people with mental health problems. Data were collected via a self-administered questionnaire by the platform Qualtrics, distributed between February and June 2024 through Instagram, WhatsApp, and Twitter.

2.5. Ethical Considerations

The study adhered to the Declaration of Helsinki (Declaration of Helsinki of the WMA – Ethical Principles for Medical Research Involving Human Participants, 2024) for research involving human subjects. The project was approved by the Human Research Ethics Committee (CEISH) of the Universitat Jaume I under code CEISH/134/2023.

2.6. Data Analysis

To assess the internal reliability of the questionnaire, variables were recoded, obtaining a Cronbach’s alpha coefficient of α = 0.860, indicating that the CAMI-S scale is reliable and suitable for measuring public stigma in the sample analysed. For descriptive analysis, qualitative variables were evaluated by frequencies and percentages, and quantitative variables were expressed as the median, interquartile range (IQR), and minimum and maximum values. For the total CAMI-S score, the mean and standard deviation were used, given its normal distribution.
The Kolmogorov-Smirnov test revealed that the variables did not follow a normal distribution. Therefore, non-parametric tests were applied for bivariate analyses (Mann-Whitney U test, Spearman’s correlation coefficient, and the Kruskal-Wallis’s test)
The software G*Power 3.1 was used to calculate the effect size (Cohen’s d) and estimate the statistical power (1-β) for comparisons that yielded statistically significant results. Effect sizes were interpreted following Cohen (1988) recommendations (Around 0.20: small effect, around 0.50: medium effect, above 0.80: large effect) [16].
Lastly, a multiple linear regression analysis was conducted using the backward stepwise method to identify the sociodemographic variables that best predict the total CAMI-S score. Independent variables were transformed into dummy variable (0 or 1, based on specific categories). The model’s goodness of fit was assessed using the adjusted coefficient of determination (Adjusted R2). The statistical analysis was carried out using IBM SPSS Statistics (version 29). A significance level of p ≤ 0.05 was applied in all cases.
Related to the use of generative artificial intelligence (GenAI) the authors have used it for spell checking, grammar revision, and punctuation of the final manuscript.

3. Results

The study included a sample of 404 participants, of which 71% (n = 287) identified as women, and 29% (n = 117) identified as men. No participant identified as non-binary. The median age was 38 years (IQR 49–26), with a range from 18 to 78 years. Additionally, most participants (86.4%, n = 349) lived in urban areas.
Regarding marital status, the 42.8% (n = 173) were married, 28.7% (n = 116) were in a relationship, 28% (n = 113) were single, and the 0.5% (n = 2) were widowed. In relation to parenthood, 50.5% (n = 204) reported not having children. In terms of education, the 55.9% (n = 226) had university studies, 31.7% (n = 128) had secondary education, 11.4% (n = 46) had primary education, and a 1% (n = 4) had no formal education.
As for employment status, the majority were economically active (75.2%, n = 304). Furthermore, 90.6% (n = 366) of participants had never experienced mental health problems, while 9.4% (n = 38) reported having a mental health diagnosis. In addition, 53.2% (n = 215) had no close contact with people with mental health problems, while 46.8% (n = 189) did.
The mean total score of the CAMI-S scale was 84.89 ± 11.122. The results of each item from the CAMI-S questionnaire are presented as supplementary information. Tables of supplemental material (S1-S3).
The following tables show the bivariate analysis exploring the relationship between sociodemographic variables and the items of the CAMI-S scale, as well as the effect size and statistical power in significant cases. The items are grouped according to the evaluated factor as carried out by Sastre-Rus, et., [14].
Table 1 presents the results of factor 1 (Integration and Contact) based on sociodemographic variables that characterize the sample.
Table 1. Statistical results for items related to Factor 1 (Integration and Contact).
Table 1. Statistical results for items related to Factor 1 (Integration and Contact).
Item Gender Age Place of residence Marital status
Value (U)
(p-value) 		d
(1-β)		 Value (r)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)
2 19648.5
(0.006)* 		0.160
(0.735)		 - 0.005
(0.915)		 0.398
(0.820)		 2.649
(0.449)
7 18287.5
(0.123)		 0.212
(< 0.001)** 		0.460
(1.000)		 8.475
(0.014)* 		0.528
(1.000)		 3.662
(0.3)
8 18058.5
(0.129)		 0.109
(0.028)* 		0.330
(0.999)		 8.949
(0.011)* 		0.310
(0.999)		 0.323
(0.956)
12 15532.5
(0.132)		 0.006
(0.898)		 2.314
(0.314)		 2.137
(0.544)
15 17048
(0.803)		 0.067
(0.182)		 2.168
(0.338)		 1.626
(0.654)
16 18884
(0.02)* 		0.128
(0.515)		 0.060
(0.226)		 3.992
(0.136)		 9.236
(0.026)* 		0.439
(0.999)
17 17913.5
(0.085)		 0.043
(0.392)		 0.837
(0.658)		 3.596
(0.308)
18 18717.5
(0.013)* 		0.132
(0.543)		 0.094
(0.059)		 1.644
(0.440)		 3.913
(0.271)
19 18002.5
(0.065)		 0.031
(0.540)		 2.571
(0.277)		 2.972
(0.396)
Note. Mann-Whitney U test (U); Spearman's correlation coefficient (r); Kruskal-Wallis test (K); Effect size (Cohen's d); Statistical power (1-β); p-value ≤ 0.05*; p-value < 0.001**.
Table 1. (bis). Statistical results for items related to Factor 1 (Integration and Contact).
Table 1. (bis). Statistical results for items related to Factor 1 (Integration and Contact).
Item Educational level Employment status "Mental disorder" Close contact
Value (K)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)
2 32.766
(< 0.001)** 		0.390
(0.999)		 5.980
(0.113)		 4573.5
(< 0.001)** 		0,408
(1.000)		 15020
(< 0.001)** 		0.249
(0.989)
7 5.212
(0.157)		 17.265
(< 0.001)** 		0.464
(1.000)		 6059
(0.152)		 18413.5
(0.075)
8 23.732
(< 0.001)** 		0.335
(0.999)		 6.271
(0.099)		 5644
(0.015)* 		0.275
(0.998)		 19162
(0.209)
12 19.84
(< 0.001)** 		0.267
(0.971)		 15.439
(0.001)** 		0.530
(1.000)		 8009
(0.05)* 		0.184
(0.856)		 23087
(0.003)* 		0.198
(0.909)
15 8.973
(0.03)* 		0.200
(0.764)		 3.380
(0.337)		 6170
(0.239)		 17618
(0.018)* 		0.187
(0.870)
16 6.940
(0.074)		 6.417
(0.093)		 6171
(0.177)		 16452,5
(< 0.001)** 		0.212
(0.944)
17 9.439
(0.024)* 		0.316
(0.997)		 6.402
(0.094)		 6637.5
(0.451)		 18358
(0.006)* 		0.258
(0.993)
18 14.847
(0.002)* 		0.379
(0.999)		 4.043
(0.257)		 5622
(0.007)* 		0.316
(0.999)		 16863
(< 0.001)** 		0.294
(0.999)
19 3.003
(0.391)		 1.697
(0.638)		 6195
(0.073)		 19522.5
(0.272)
Note. Mann-Whitney U test (U); Spearman's correlation coefficient (r); Kruskal-Wallis test (K); Effect size (Cohen's d); Statistical power (1-β); p-value ≤ 0.05*; p-value < 0.001**.
Table 2. shows the results of factor 2 (Social Distance), also based on sociodemographic variables that characterize the sample.
Table 3. presents the results for factor 3 (Dangerousness and Avoidance) based on sociodemographic variables that describe the sample.
The final multiple linear regression model identified four significant predictors of the total variable representing community integration: gender, educational level, having a “mental disorder”, and having close contact. The adjusted coefficient of determination (Adjusted R2) was 0.109, indicating that 10.9% of the variance in the dependent variable is explained by the variables included in the model. The results obtained were as follows (Table 4).

4. Discussion

Regarding gender differences, the results indicate that women tend to exhibit lower levels of stigmatization, which translates into a greater willingness for community integration and lower perceptions of dangerousness, consistent with findings from Schroeder and Dey [17,18]. Conversely, a study conducted in Hong Kong noted that being female was associated with higher levels of stigma [19]. Based on the results, it can be inferred that personal education and culture could influence whether women or men respond more positively to a mental health problem.
Looking at other sociodemographic variables, our findings suggest that younger generations show fewer stigmatizing attitudes, although they also display a higher perception of risk. Similarly, the study by Schomerus demonstrated that social rejection of people with schizophrenia and depression increases with age [20]. Other studies also found that older individuals presented higher levels of public stigma, attributing this result to lower education levels and limited mental health literacy [19,21].
The analysis of the place of residence was only statistically significant for two items: “Mental illness is an illness like any other, and a more tolerant attitude is needed towards people with mental disorder”. The results show that people living in urban areas exhibit greater social rejection. However, previous studies argue that rural populations show higher levels of stigma, attributing it to a lack of education, poor dissemination of information on the subject, and limited access to healthcare services in rural communities [17,22].
Regarding marital status, in our study, married individuals expressed more agreement with the idea that people with mental health problems should be part of the community. This contrasts with a previous study conducted with a population of people with mental health problems, and for them, living with a partner increased stigmatizing attitudes [23]. Therefore, there does not seem to be agreement between what the population indicates and what the patients perceive.
In terms of educational level, having university-level studies was associated with lower stigma and greater support for community integration. This finding aligns with other authors who found that people with lower educational levels showed more stigmatizing behaviours [19,23]. Similarly, in a university context, Ruiz et al. found that students in health sciences degrees had fewer stigmatizing attitudes than those in economics or data science degrees [24].
Regarding employment status, active workers displayed lower levels of stigma, which is consistent with the findings from Alonso et al. [23]. Furthermore, a study in southwestern Ethiopia reported that people with paid employment show more stigmatizing attitudes, and housewives are more empathetic and less authoritarian than other groups [25].
Additionally, in our study, parenthood was associated with stronger prejudices related to dangerousness and avoidance of people with mental health problems. This finding is in line with Isbell et al., who found that parents perceived the symptoms of Disruptive Mood Dysregulation Disorder as more severe and showed more critical attitudes, especially regarding the use of medication. This greater sensitivity might stem from protective instincts but may also manifest as stigmatization [26]. However, another study did not find significant differences based on whether participants had children [27]. The variability among results highlights the need for further research on the influence of parenthood on public stigma.
The last two sociodemographic variables studied in our study show that having personally experienced a mental health problem, as well as maintaining close contact with someone with a diagnosis, fosters more positive and less risk-based perceptions. Direct experience with mental illness acts as a negative moderator, reducing both anxiety and perceptions of dangerousness [21]. In our research, we also found that people with relatives or friends affected by mental illness tend to exhibit lower stigma and may play a key role as community advocates; these findings agree with Reta et al., who observed that familiarity is linked to greater benevolence and lower stigma [25].
However, it is important to note that contact alone does not always lead to non-stigmatizing attitudes. For example, Yuan et al. found that even mental health professionals in Singapore -despite their experience- still endorsed certain stereotypes [28]. This suggests that the reduction of stigma requires more than just exposure and may depend on cultural, structural, and educational factors.
Furthermore, in our population, the mean global stigma score among participants was 84.89, indicating a clear trend toward favourable attitudes regarding community integration of people with mental health problems. López et al., used the same scale to evaluate stigma in medical residents and obtained a mean score of 86.7 [29,30]. The similarity in results reinforces a general trend toward less stigmatizing attitudes.
In terms of statistical power, the variables of age, place of residence, marital status, educational level, employment status, "mental disorder", and close contact showed statistically significant associations supported by 1-β values greater than 0.80. This reinforces the robustness of our findings, suggesting that the observed relationships are unlikely to be due to chance and are potentially replicable in similar contexts.
Finally, the multivariate model indicates that identifying as a woman, having a secondary or university education, having a "mental disorder", and maintaining close contact are all positively associated with higher scores on the total variable, reflecting more favourable attitudes toward community integration of people with mental health problems. Although the model explains only 10.9% of the variability, the individual predictors are significant and relevant. Nevertheless, other unmeasured factors may also influence stigma.
The authors consider it appropriate to indicate that the study has certain limitations. Firstly, the use of non-probabilistic convenience sampling limits the ability to generalize the results to the entire Spanish adult population. In addition, the online data collection may have introduced self-selection bias, as it is likely that only people with access to social media.

5. Conclusions

A favourable trend is observed towards the community integration of people with mental health problems, although relevant differences were identified based on sociodemographic variables.
  • Women show lower levels of stigmatization, reflecting greater willingness for community integration.
  • Older age is associated with increased stigma, while younger individuals perceive more risk.
  • Married people are more likely to support community participation of people with mental health issues.
  • Not having children is associated with less prejudiced attitudes regarding dangerousness and avoidance.
  • A higher educational level is linked to reduced stigma and greater support for social inclusion. Additionally, less stigmatizing attitudes are found among those living in rural or semi-rural areas, as well as among economically active individuals.
  • Lastly, both the personal experience of having a “mental disorder” and close contact with someone with a mental health condition promote more positive and empathetic perceptions.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org, Table S1: CAMI-S scale items related to Factor 1 (integration and contact), Table S2. CAMI-S Items Related to Factor 2 (Social Distance), Table S3. CAMI-S Items Related to Factor 3 (Dangerousness and Avoidance).

Author Contributions

The four authors contributed equally to the conceptualization and methodological design of the study. Authors Lorena Liñan-Díaz and Nuria Vives-Díaz conducted the sampling and database generation process, which was subsequently analyzed by the four authors of the article, who jointly generated and interpreted the results. Authors Lorena Liñan-Díaz and Nuria Vives-Díaz drafted the manuscript. The authors María Desamparados Bernat-Adell and Vicente Bernalte-Martí reviewed the final version of the manuscript. All phases of the study have been completed under the supervision of María Desamparados Bernat-Adell. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study adhered to the Declaration of Helsinki (Declaration of Helsinki of the WMA – Ethical Principles for Medical Research Involving Human Participants, 2024) for research involving human subjects. The project was approved by the Human Research Ethics Committee (CEISH) of the Universitat Jaume I, under code CEISH/134/2023.

Acknowledgments

The authors thank all the participants for their anonymous and selfless collaboration.

Conflicts of Interest

The authors declare no conflicts of interest.

Public Involvement Statement

Participants were recruited through social media platforms such as Instagram, WhatsApp, and Twitter. Ultimately, 404 individuals were collected throughout the sampling period.

Guidelines and Standards Statement

This manuscript was drafted against the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.

Use of Artificial Intelligence

Related to the use of generative artificial intelligence (GenAI), the authors have used it for spell checking, grammar revision, and punctuation of the final manuscript. AI has not been used for any purpose other than the one previously mentioned.

Abbreviations

The following abbreviations are used in this manuscript:
DSM-V-TR Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision
ICD-11 International Classification of Diseases, 11th Edition
CAMI-S Community Attitudes towards Mental Illness
IBM SPSS International Business Machines Sta
SPSS Statistical Package for the Social Sciences
IQR Interquartile range
CEISH Ethics Committee for Research Involving Human Subjects
STROBE Strengthening the Reporting of Observational Studies in Epidemiology

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Table 2. Statistical results of items related to Factor 2 (Social Distance).
Table 2. Statistical results of items related to Factor 2 (Social Distance).
Item Gender Educational level Employment status "Mental disorder" Close contact
Value (U)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)
1 18100.5
(0.12)		 4.649
(0.199)		 0.955
(0.812)		 6277
(0.212)		 19100.5
(0.189)
3 18483
(0.064)		 16.914
(< 0.001)** 		0.319
(0.998)		 1.406
(0.704)		 5560
(0.018)* 		0.172
(0.803)		 18273
(0.042)* 		0.149
(0.661)
4 15440
(0.154)		 9.983
(0.019)* 		0.274
(0.978)		 5.735
(0.125)		 8928.5
(0.001)* 		0.317
(0.999)		 25093.5
(< 0.001)** 		0.304
(0.999)
5 14069.5
(0.008)* 		0.158
(0.719)		 13.633
(0.003)* 		0.330
(0.999)		 6.548
(0.088)		 8896.5
(0.003)* 		1.187
(1.000)		 24563.5
(< 0.001)** 		0.222
(0.963)
6 14989
(0.075)		 16.773
(< 0.001)** 		0.306
(0.995)		 4.147
(0.246)		 8136.5
(0.069)		 24369
(< 0.001)** 		0.196
(0.903)
13 13558.5
(0.001)* 		0.170
(0.788)		 24.387
(< 0.001)** 		0.332
(0.999)		 16.179
(0.001)* 		0.409
(0.999)		 8334
(0.029)* 		0.186
(0.867)		 25150.5
(< 0.001)** 		0.300
(0.999)
14 19352.5
(0.007)* 		0.179
(0.837)		 17.411
(< 0.001)** 		0.417
(0.999)		 2.697
(0.441)		 5312.5
(0.007)* 		0.195
(0.900)		 17206
(0.003)* 		0.178
(0.829)
Note. Mann-Whitney U test (U); Spearman's correlation coefficient (r); Kruskal-Wallis test (K); Effect size (Cohen's d); Statistical power (1-β); p-value ≤ 0.05*; p-value < 0.001**.
Table 3. Statistical results of items related to Factor 3 (Dangerousness and Avoidance).
Table 3. Statistical results of items related to Factor 3 (Dangerousness and Avoidance).
Item Gender Age Children Educational level Employment status "Mental disorder" Close contact
Value (U)
(p-value) 		d
(1-β)		 Value (r)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (K)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)		 Value (U)
(p-value) 		d
(1-β)
9 19607
(0.005)* 		0.164
(0.757)		 - 0.013
(0.792)		 22873
(0.026)* 		0.141
(0.605)		 23.186
(< 0.001)** 		0.297
(0.993)		 6.807
(0.078)		 5721.5
(0.057)		 16023.5
(< 0.001)** 		0.214
(0.949)
10 15438
(0.095)		 0.072
(0.147)		 18565.5
(0.04)* 		0.129
(0.525)		 28.555
(< 0.001)** 		0.305
(0.995)		 7.031
(0.071)		 7934.5
(0.06)		 22739
(0.007)* 		0.188
(0.874)
11 14433.5
(0.018)* 		0.124
(0.487)		 - 0.097
(0.05)* 		0.311
(0.999)		 19447.5
(0.385)		 6.262
(0.1)		 2.201
(0.532)		 8674
(0.007)* 		0.179
(0.836)		 23681.5
(0.002)* 		0.161
(0.739)
20 16075.5
(0.337)		 0.090
(0.071)		 18660
(0.034)* 		0.469
(1.000)		 27.667
(< 0.001)** 		0.589
(1.000)		 14.154
(0.003)* 		0.278
(0.982)		 8121
(0.015)* 		0.323
(0.999)		 22695.5
(0.004)* 		0.453
(1.000)
Note. Mann-Whitney U test (U); Spearman's correlation coefficient (r); Kruskal-Wallis test (K); Effect size (Cohen's d); Statistical power (1-β);p-value ≤ 0.05*; p-value < 0.001**.
Table 4. Unstandardized coefficients of the final model.
Table 4. Unstandardized coefficients of the final model.
Variable Coefficient (β) Standard Error p-value
Constant (Total) 85.001 2.518 < 0.001
Gender (Woman) 2.523 1.205 0.037
Educational level (Secondary and University) 5.061 1.604 0.002
“Mental disorder” (Yes) 4.535 1.831 0.014
Close contact (Yes) 4.183 1.123 < 0.001
Note. Multiple linear regression, backward stepwise method.
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