Understanding and Addressing Prejudices Faced by Mentally Ill Individuals: A Multidimensional Analysis

Understanding and Addressing Prejudices Faced by Mentally Ill Individuals: A Multidimensional Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding and Addressing Prejudices Faced by Mentally Ill Individuals: A Multidimensional Analysis Mohammad Irfan Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014975/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mental illness stigma is a pervasive issue that transcends cultural and national boundaries, presenting substantial impediments to successful treatment, reducing key life opportunities, and exacerbating poor outcomes beyond the direct effects of the illness itself. This research primarily investigated the prejudices associated with mental illness, focusing on their combined manifestation through explicit and implicit biases. It aimed to demonstrate how these prejudices contribute to discrimination, thereby aggravating the primary symptoms of mental disorders. Additionally, the study explored the most efficacious intervention strategies aimed at mitigating these biases. The assumption was that participants with priorly direct contact with mentally ill individual would demonstrate significant reduction in their prejudice level. The sample ( n = 408 ) consisted of Nepalese individuals from diverse demographic backgrounds, aged between 18 and 60. They initially completed the Prejudice towards People with Mental Illness (PPMI) scale measuring explicit prejudice, and the mental illness Implicit Association Test (IAT) assessing implicit prejudice. Subsequently, they were randomly assigned to one of four distinct groups: direct contact, indirect contact, education empowerment, and a control group; each incorporating interventions except control group. After two weeks of corresponding exposures, both tests were readministered to evaluate changes in scores. The differences in both scores were calculated to determine the impact of interventions. A Kruskal-Wallis test for changes across groups indicated there was a significant difference, \(\:\chi\:2\:\left(3,\:\:408\right)=(362.849;\:341.135),\:p<0.001\) for PPMI and IAT scores respectively. Post-hoc comparisons using Dunn’s method with a Bonferroni correction for multiple tests indicated that the mean changes in PPMI and IAT for the group engaging in direct contact with mentally ill individual were significantly lower than other groups. This implies that stigma reduction programs should incorporate direct interaction with individuals who have experienced mental illness. Given the uncertain long-term effectiveness of these interventions, it is essential to conduct extended research to evaluate their sustained impact. Mental illness stigma prejudice PPMI IAT interventions effectiveness Figures Figure 1 Figure 2 Figure 3 1. Introduction Unlike other illnesses, mental illness is not a monolithic concept but rather a complex and multifaceted phenomenon encompassing a wide array of psychological, emotional and behavioral disorders. Probably, the ramification of this inherent complexity often leads to misconceptions and biases; thereby, contributing to the stigmatization and discrimination of individuals and their families with mental health conditions. Despite advancements in our understanding of mental illness and overwhelming anti-stigma maneuvers over the past few decades, misconceptions, biases, and discriminatory attitudes continue to hinder the well-being and societal integration of those affected. This study endeavors to delve into the multifaceted nature of these prejudices faced by mentally ill individuals, aiming not only to comprehend the root causes but also to propose effective strategies for their mitigation. The study’s focus on identifying sustainable interventions offers valuable insights for policymakers, healthcare providers, and educators. Ultimately, this research has the potential to significantly enhance the quality of life for individuals with mental health conditions by promoting a deeper understanding and reducing the societal barriers they face. Historically, mental illness has been misunderstood and feared, with misconceptions shaping societal attitudes and responses over time. In many ancient cultures, mental illness was often attributed to supernatural forces or the influence of spirits and demons; for example, the ancient Greeks and Romans sometimes saw it as a form of divine punishment or demonic possession (Fabrega, 1990 ). Similarly, during the Middle Ages in Europe, mental illness was frequently viewed through a religious lens, with many attributing such conditions to witchcraft or possession by evil spirits. Throughout various periods, particularly in Western societies, mental illness was also sometimes perceived as stemming from moral or character flaws, a perspective that persisted into the 18th and 19th centuries, where societal and moral judgments were often placed on individuals exhibiting symptoms of mental health disorders (p. 294). These archaic views have evolved, yet remnants of these misconceptions persist in modern societies. For example, mentally ill individuals in Nepalese society are ubiquitously labelled as ‘ alter ’, ‘ crazy ’, or ‘ mad ’. Stigmatizing attitudes pervade nearly every aspect of life for those affected by mental illness (Kenny & Bizumic, 2016 ). In healthcare settings, individuals with mental health conditions often face unwarranted judgments, leading to misdiagnoses or inadequate treatment (Bizumic et. al., 2022 ). For instance, healthcare providers may inadvertently attribute physical symptoms to the mental illness itself rather than thoroughly investigating potential underlying physical health conditions, resulting in meagre care (Kolb et. al., 2023 ). In the realm of employment, individuals may encounter discrimination that restricts job opportunities and impedes career progression (Young et. al., 2019 ). Employers may be reluctant to hire or promote individuals with mental health issues due to baseless concerns about their reliability or productivity, leading to underemployment and financial instability (Balogun-Mwangi, 2023). Within educational settings, students with mental health issues might be unjustly labelled or receive insufficient support, impeding their academic progress. Educators and administrators often lack the necessary training to identify and accommodate mental health needs, resulting in a dearth of appropriate interventions and support services (Reavley et. al., 2017 ). Interpersonal relationships are also strained by stigma, affecting interactions with family, friends, and colleagues (Yates & Gatsou, 2021 ). Loved ones may distance themselves due to misconceptions about mental illness, and colleagues might avoid collaborating closely with someone known to have a mental health condition, exacerbating social isolation and weakening support networks (Fox et. al., 2018 ). The most detrimental consequence of these stigmas is their exacerbation of the illness's severity, often overshadowing the primary symptoms of the disorder itself. Experienced stigma, characterized by direct discrimination and negative attitudes from others, leads to social isolation, reduced access to healthcare, and diminished opportunities in employment, education, and social interactions (Evans et. al., 2024 ). This form of stigma can result in individuals feeling marginalized and alienated, further exacerbating mental health symptoms and impeding recovery. Anticipated stigma, or the fear of being stigmatized, can deter individuals from seeking essential treatment and support due to concerns about being judged or discriminated against (Adu et. al., 2021 ). This apprehension can prevent individuals from accessing early intervention and continuous care, which are crucial for effectively managing mental health conditions. Affiliate stigma, experienced by family members and close associates of individuals with mental condition, further complicates the issue (Li et. al. 2022 ). These individuals often face social rejection and prejudice due to their association with someone with a mental health condition, which can lead to additional stress and emotional burden on the support network, thereby indirectly impacting the person with the illness (Shahwan et. al., 2022 ). Self-stigma, wherein individuals internalize societal prejudices, further compounds the issue by diminishing self-esteem and self-efficacy (González-Sanguino et. al., 2021 ). Internalized stigma induces feelings of shame, guilt, and worthlessness, making individuals less likely to seek help and more likely to withdraw from social interactions (Arboleya-Faedo et. al., 2023 ). This internal conflict can further lead individuals to undermine their own illness or treatment by avoiding professional help, failing to adhere to prescribed regimens, or discontinuing treatment prematurely (Schomerus et. al., 2019 ). Consequently, the prognosis of such disorders due to aforementioned dimensions of stigma becomes severely compromised further cascading into severe mental illness which could have been easily ameliorated otherwise. A wide body of literature has been dedicated to the conceptualization and measurement of mental illness stigma. Various methodologies have been employed, including qualitative studies, surveys, and experimental designs. Qualitative studies provide in-depth insights into personal experiences of stigma, capturing the nuanced ways in which individuals perceive and are affected by stigma (FitzGerald et., al., 2019; Maunder & White, 2019 ; Thornicroft, et. al., 2016 ). Surveys offer broad data on the prevalence and impact of stigma across different populations, enabling researchers to identify widespread patterns and correlations (Sheppard et. al., 2023 ; Bayındır et. al., 2023 ; Fung et. al., 2022 ; Poulgrain et. al., 2022 ; Fang et. al., 2021 ; Klik et. al., 2019 ). Experimental designs have been used to test interventions aimed at reducing stigma, providing empirical evidence on the effectiveness of different strategies (Cho & Kim, 2024 ; Atienza-Carbonell, 2022; Görzig & Ryan, 2022 ; Subramanian & Santo, 2021 ; Brown & Russel, 2019; De Witt et. al., 2019 ). However, despite the substantial focus on stigma research, there remains a significant gap in literature. Many studies are limited to a single agenda, either focusing exclusively on measurement, conceptualization, or interventions, rather than providing a comprehensive view that integrates these aspects. Moreover, such studies are often lacking the integration of explicit and implicit prejudices in their measurement, conceptualization, and intervention processes. Explicit biases can be moderated by participants during research, thereby obscuring underlying evaluations. Meanwhile, the prevalence of implicit biases alone does not always lead to provoking discriminatory behavior. Therefore, it is equally important to incorporate implicit measures to avoid the influence of control-related responses in explicit measures and understand what extent of underlying implicit biases result in expression of detrimental behaviors towards individuals with mental health conditions. The current research study was of paramount importance as it aimed to fill this wide gap. In addition, by experimentally developing and evaluating strategies to effectively reduce both forms of stigma, this research aimed to improve mental health outcomes and foster a more inclusive society thereafter. Accordingly, this study posited several key hypotheses aimed at understanding and addressing mental health stigma: H 1 A statistically significant positive correlation exists between PPMI and IAT scores across diverse demographic groups. H 2 People with a history of mental illness are anticipated to demonstrate significantly lower scores on both the PPMI and IAT measures compared to those without such a history. H 3 Participants with a medical education background are expected to exhibit significantly lower scores on both the PPMI scale and the IAT in contrast to individuals from other educational backgrounds. H 4 Individuals who undergo a direct contact intervention program are expected to demonstrate a significantly greater reduction in scores on both the PPMI and IAT measures compared to those in control and alternative intervention groups. 2. Methods 2.1 Study design The focus of this study was to elucidate the complex interplay of explicit and implicit prejudices in maintaining and perpetuating stigmas against people with mental health conditions and determine the most efficacious strategies to ameliorate those attitudes; therefore, this research unfolded in two distinct phases to provide a comprehensive understanding and evaluate effective interventions to combat stigma. Phase 1 commenced by quantifying explicit prejudices through the PPMI scale (Kenny et al., 2018 ), succeeded by the assessment of implicit biases using the Mental Illness IAT (Borchert, 2022 ). This dual quantitative approach endeavored to holistically grasp the intricate interplay of explicit and implicit biases that fuel the perpetuation and maintenance of stigma against individuals wrestling with mental disorders. Also, these initial assessments provided crucial data points for comparing changes in prejudices across all groups. The study then pivoted in Phase 2 , towards experimentally pinpointing efficacious interventions to combat such stigmas. The same participants were randomly assigned to one of four distinct groups based on the type of interventions they receive: direct contact, indirect contact, educational empowerment, or a control condition. Each intervention cohort then engaged in a structured regimen, followed by re-administration of both tests after two weeks to gauge the transformative impact of the interventions. Randomization ensured the impartial allocation of participants and the equal distribution of any unknown extraneous variables across intervention groups, while control group served as a baseline comparison tool. Additionally, demographic variables were controlled to account for potential confounding factors. 2.1.1 Direct Contact Participants in this group interacted with an individual who had personally navigated the challenges of major depression with suicidal ideation. Over the course of two weeks, she candidly shared her journey, providing an in-depth narrative that covered the onset and progression of her symptoms, the profound impact of her diagnosis, and her three-month stay in a psychiatric ward. This comprehensive account included her experiences with various treatment modalities such as pharmacological interventions, counseling, and therapeutic approaches, including TMS[1] . She delved into the intricacies of living with mental illness, highlighting the daily battles she faced, not only with the illness itself but also with the pervasive stigma that often accompanies such diagnoses. By sharing her personal struggles and triumphs, she offered a raw and authentic perspective on the realities of mental health conditions. In addition to recounting her personal experiences, she discussed her role as an advocate for mental health awareness. She explained how her journey inspired her to write the novel, Sanely Insane (Awasthi, 2016 ), which serves as a powerful tool to combat mental illness stigma. The novel sheds light on common myths and misconceptions about mental illness and emphasizes that, contrary to popular belief, most cases are treatable with the right interventions. Participants in this group could engage in meaningful dialogues with her, asking questions and discussing their own perceptions and biases. This group was designed to humanize the experience of mental illness and foster empathy and understanding, challenging the preconceived notions that often contribute to stigma. 2.1.2 Indirect contact Not everyone has the chance to engage in real-life interactions with individuals experiencing mental health challenges; therefore, virtual contact serves as an effective alternative. Participants in this group engaged with the film Taare Zameen Par [2] [Like Stars on Earth] (Khan, 2007 ). The movie portrays the journey of a young boy grappling with dyslexia, offering a poignant depiction of the struggles including stigma, endured by individuals contending with mental health challenges and the transformative influence of supportive interventions. It was presented in seven carefully selected segments over a two-week period, each highlighting unique themes, allowing participants to reflect on the experiences depicted in the film and draw parallels to real-world attitudes towards mental health (see Table A1 ). Participants engaged with each segment on alternate days, affording them opportunities for contemplation and group dialogue the subsequent day. For instance, following the viewing of segment 1 on day 1, participants convened for a collective discussion on day 2 before proceeding with the subsequent segment on day 3, continuing for 14 days. This iterative approach was designed to provide participants with a profound understanding of mental health stigma and the impact of supportive environments. 2.1.3 Education empowerment Participants in this group engaged in a comprehensive series of structured educational sessions, meticulously designed to enhance their understanding of mental health issues over a two-week period. These sessions were thoughtfully curated to cover a broad spectrum of topics, providing a holistic and in-depth exploration of mental health. The program began with foundational knowledge, aiming to establish a robust understanding of mental health conditions. During the first week, the group delved into the nature and classification of various mental health disorders, discussing their symptoms, causes, and prevalence. This included an exploration of the biological, psychological, and social factors contributing to mental illnesses. Participants also learned about the latest research findings and the scientific basis for different treatment approaches, including pharmacological interventions, psychotherapy, and emerging therapeutic modalities such as brain stimulation techniques, mindfulness, and exercise. Meanwhile, they explored the social dimensions of mental health, discussing the pervasive effects of stigma, the critical role of social support, and effective strategies for advocacy and empowerment in the second week. Participants were also encouraged to reflect on their attitudes and beliefs about mental illness, share personal experiences, and actively participate in a supportive and empathetic learning environment. These sessions, in both weeks, were thoughtfully designed to be highly interactive, incorporating group activities and discussions to foster engagement and enhance information retention. Through these interactive sessions, common myths and misconceptions about mental health were debunked, effectively reducing stigma and fostering empathy. 2.1.4 Control group Participants in the control group did not receive any specific intervention during the two-week period. Instead, they continued with their usual routines without exposure to the structured sessions or educational content provided to the experimental groups. However, they were subject to the same assessment procedures as the other groups. Initially, they completed both tests to establish their baseline levels of prejudices. Throughout the two-week period, the group was monitored to ensure they were not inadvertently exposed to any intervention-related content. This monitoring helped to maintain the integrity of the control condition by preventing any external influences that could affect their scores. The inclusion of the control group is critical for isolating the effects of the specific interventions on reducing mental health stigma. By comparing the changes in prejudices between the control and experimental groups, the study could attribute any observed differences to the impact of the interventions rather than to external factors or natural variations over time. 2.2 Participants and procedure The current research was carried out in Nepal, a south-east Asian country with diverse population. A priori power analysis using G*Power V3.1.9.7 was performed to determine the necessary sample size (Faul et al., 2020 ). A minimum sample of 279 was required to detect a medium effect size with 80% power for ANCOVA with 4 distinct groups and a covariate. The recruitment campaign encouraging potential participants was circulated on social media platforms, online forums, and mental health organizations targeting the diverse Nepalese community to join. It highlighted the study's aims and the importance of understanding and addressing mental health stigma. Initially, an attempt was made to recruit participants randomly; however, due to the stigma associated with mental health, this approach yielded a limited pool of participants. Consequently, convenience sampling was employed, targeting individuals readily accessible to the researcher. Additionally, word-of-mouth referrals, where initial participants were encouraged to refer friends and family members. This method facilitated the recruitment of a broader and more varied sample, enhancing the representativeness and generalizability of the study findings. 2.3 Measures 2.3.1 Demography Initially, a substantial number of individuals ( n = 437 ) responded to participate in the research. However, following a thorough debriefing session, a few ( n = 29 ) chose to withdraw from the study. Remarkably, all participants who opted to proceed remained committed throughout the study's duration, providing comprehensive data across both phases. Table 1 presents a detailed summary of the sample demographics. Participants for this study were selected based on specific criteria to ensure the relevance and accuracy of the findings. All participants were Nepalese, aged between 18 and 60 years, and capable of providing informed consent. Inclusion criteria required participants to have no prior diagnosis of severe cognitive impairments or neurological disorders that could affect their ability to comprehend and complete the assessments. Additionally, individuals currently undergoing treatment for acute psychiatric conditions were excluded to prevent any interference with their ongoing therapies. The study aimed to encompass a diverse sample in terms of gender, socioeconomic status, and educational background, ensuring a broad representation of the Nepalese population. Participants were also required to have a basic understanding of mental health issues, either through personal experience or general awareness, to facilitate meaningful engagement with the study's interventions and assessments. Table 1 Sociodemographic Characteristics of Participants Characteristics Frequency Percentage (n) (%) Gender Male Female Non-binary 194 47.5 213 52.2 1 0.2 Highest Qualification High School 79 19.4 Some college/university degree or vocational training 83 20.3 Bachelors 95 23.3 Masters 80 19.6 Medical degree (MBBS/BSc Nursing, MD/MS/MCh) 71 17.4 Occupation Student 196 48 Employed 212 52 History of Mental Illness Yes 162 39.7 No 196 48 Prefer not to say 50 12.3 Experience with Mental Health Services Yes 182 44.6 No 193 47.3 Prefer not to say 33 8.1 2.3.2 PPMI This scale is a widely used self-report questionnaire designed to measure explicit prejudices against individuals with mental health conditions. It has even been adjusted to measure specific mental illnesses such borderline personality disorder (Sheppard et. al, 2023 ), schizophrenia and depression (Bizumic et. al., 2022 ). This scale comprises 28 items, each rated on a Likert scale ranging from − 2 (strongly disagree) to 2 (strongly agree). The PPMI Scale captures various dimensions of prejudice through its four subscales: Fear/Avoidance[3] (8 items), Unpredictability[4] (6 items), Authoritarianism[5] (6 items), and Malevolence[6] (8 items). The scale and its subscales are balanced with equal numbers of positively and negatively keyed items, avoiding double-barreled items. After reversing some scores as mentioned in the scale, the PPMI score was calculated by averaging the scores obtained in all 28 items. While the study used the overall PPMI scores to assess explicit prejudices, detailed analyses of the subscales were not the primary focus of this research. The PPMI Scale has demonstrated strong internal consistency and reliability across diverse populations. It has high construct validity, accurately measuring the concept of prejudice towards mental illness. Cronbach's alpha coefficients typically range from 0.85 to 0.92, indicating excellent reliability (Kenny et. al, 2018 ). Test-retest reliability over a two-week period has also been reported to be high, demonstrating stability over time (p. 11). 2.3.3 Mental illness IAT The IAT is a widely used cognitive-behavioral paradigm that measures the strength of automatic [implicit] associations between concepts in people’s minds relying on latency measures in a simple sorting task (Greenwald et. al., 1998 ). The test involved a series of categorization tasks where participants quickly classify words and attributes related to two different pairings. The strength of an association between concepts is measured by the standardized mean difference (d-score) of the hypothesis-inconsistent and hypothesis-consistent pairings (Greenwald et.al., 2003 ). Mental Illness IAT is a computerized measure developed by Millisecond Software using this same principle to assesses implicit biases towards individuals with mental illness (Borchert, 2022 ). Basically, the individuals with stronger implicit biases will be quicker to associate negative words with mental illness and positive words with physical. The validity of the IAT has been supported through numerous studies showing that IAT scores predict discriminatory behavior and attitudes. The reliability of the IAT is generally good, with internal consistency coefficients (split-half reliability) typically ranging from 0.7 to 0.9 (Jordan, 2020 ). Its predictive validity, or the ability to predict relevant outcomes (e.g., behavior), further underscores its utility in psychological research. By employing these validated and standardized tools within this study's sample, the research ensured a comprehensive and reliable assessment of both explicit and implicit prejudices towards individuals with mental health conditions. These measurements were critical for understanding the baseline levels of stigma and evaluating the impact of the intervention programs on reducing mental health stigma. 2.4 Data analysis 2.4.1 Research methods and Variables The methodology in this study predominantly relied on quantitative approaches, enabling a rigorous analysis of explicit and implicit biases and ensuring statistical robustness in assessing the effectiveness of stigma-reduction interventions. While qualitative methods were not explicitly employed, open-ended questions and group discussions were utilized in both contact groups (i.e., direct and indirect). However, these qualitative interactions were not subjected to formal analysis but rather served to enhance participant engagement and deepen their understanding of mental health stigma. The dependent variables encompassed explicit prejudices, measured through scores obtained from the PPMI Scale, and implicit biases, assessed via scores from the Mental Illness IAT. These variables provided insights into individuals' overt and unconscious biases towards mental health conditions, respectively. Meanwhile, the independent variables varied depending on the phase of the study itself. For instance, pre-intervention study incorporated demographic variables such as age, gender, education level, history of mental illness, experiences with mental health services and socioeconomic status as independent variables in contrast to the types of intervention included in post-intervention study. The observed changes in scores were calculated by subtracting the pre-intervention scores from the post-intervention scores; for example, PPMI_Diff = Post_PPMI-Pre_PPMI and IAT_Diff = Post_IAT-Pre_IAT . Overall, the meticulous control and operationalization of these variables facilitated a comprehensive understanding of mental health stigma and evaluated the efficacy of interventions in addressing this pervasive issue. 2.4.2 Analysis plan The results of the research were collated, coded, and analyzed using IBM SPSS Statistics V25.0 (IBM Corporation, 2017 ). Participants’ characteristics were described using mean, standard deviation, frequency, and percentage (see Table 1 and Table 2 ). Since all participants, after the debriefing process, volunteered until the end of the research, no data adjustment was required. The data analysis process commenced with checking for outliers and assessing the normality of the data distribution using visual inspection methods, including boxplots, histograms and Q-Q plots, alongside statistical tests such as the Kolmogorov-Smirnov and Shapiro-Wilk tests (see supplementary material). These analyses collectively confirmed that the data did not adhere to a normal distribution. Consequently, non-parametric statistical tests were employed for all analyses. To explore the relationship between explicit and implicit biases, Spearman correlation analysis was conducted examining the association between PPMI and IAT scores. The Mann-Whitney U test was utilized to investigate differences in both sets of scores based on participants' history of mental illness, facilitating comparisons between individuals with and without such a history. Additionally, the Kruskal Wallis test followed by Dunn’s test was employed to determine whether participants' educational background, specifically those with a medical education, yielded significantly different scores compared to participants from other educational backgrounds in Phase 1 of the study. In Phase 2, after implementing the intervention programs, the Kruskal Wallis test was employed to discern any significant differences in post-intervention scores across all four experimental groups. This was followed by post-hoc Dunn’s tests to identify the most effective intervention strategy in reducing stigma. Additionally, the Related-Samples Wilcoxon Signed-Rank test between pre- and post-intervention scores and Independent-Samples Kruskal Wallis test in pre-intervention scores across all groups were performed, alongside Levene’s test with all variables. These additional tests eliminated potential extraneous variables influencing post-intervention scores, thereby ensuring the accuracy of the inferences drawn from the experiment. All analyses were conducted separately for PPMI and IAT scores and at the significance level of p < 0.05 in this study unless mentioned in the test statistic otherwise. 3. Theory and calculation The theories that ground this study include the Dual-Process Theory (Tversky & Kahneman, 1974 ) and Contact Theory (Allport, 1954 ). Dual-Process theory posits that human cognition operates through two distinct pathways: an automatic, unconscious system called implicit and a controlled, conscious system called explicit . This theory was significantly advanced by Daniel Kahneman and his colleague Amos Tversky in the field of behavioral psychology, particularly through their work on cognitive biases and decision-making processes (Kahneman, 2011 ; Tversky & Kahneman, 1974 ). Their research highlighted how people often rely on heuristics, or mental shortcuts, which operate implicitly through System 1 . In contrast, System 2 involves more deliberate and reflective thinking processes, which require effort and conscious control (Kahneman, 2011 ). These insights were foundational in understanding how biases can co-operate implicitly and explicitly. The existing literature on Dual-Process theory has expanded to various domains, including social psychology, where it is used to explain the persistence of stereotypes and prejudices. Researchers like Banaji and Greenwald ( 2013 ) have further explored implicit biases using IAT, demonstrating how subconscious associations can influence attitudes and behaviors even when individuals consciously endorse egalitarian beliefs. Contact theory, originally developed by Gordon Allport in 1954, posits that interpersonal contact under appropriate conditions can reduce prejudice between majority and minority group members. The theory suggests that prejudice can be reduced when individuals from different groups interact under conditions of equal status, common goals, cooperation, and institutional support (Allport, 1954 ). Subsequent research has supported and refined this theory, indicating that positive interactions with mentally ill individuals can reduce stigma and promote more favorable attitudes (Adu et. Al., 2021 ; Maunder & White 2019 ). By leveraging these theories, the research aimed to elucidate how dual cognitive processes and interpersonal contact interact to perpetuate or mitigate stigmatizing attitudes and behaviors respectively. This framework not only facilitated a nuanced analysis of prejudice but also guides the development of targeted interventions that address both conscious biases and subconscious associations. Through this lens, the study sought to offer a robust, theoretically informed approach to dismantling stigma and promoting mental health equity. Figure 1 provides an overview of this framework. 4. Results Given non-adherence of collected data to normal distribution, non-parametric statistical tests were employed for subsequent analyses. These analyses provided a comprehensive overview of the factors influencing prejudice towards mental illness and the effectiveness of intervention strategies. The results are represented sequentially, reflecting the design of the study. The variables PPMI and IAT in subsequent sections denote the scores for their corresponding studies only. For example, PPMI within Phase 1 only denotes score for Phase 1. 4.1 Phase 1 The study during this period focused on analyzing the baseline data collected from participants to understand the relationship between various demographic factors and prejudice towards mental illness, as well as to assess any existing correlations between explicit and implicit biases. The mean Pre PPMI score was 0.034 (SD = 0.558), while the mean Pre IAT score was 0.509 (SD = 0.474), indicating baseline levels of explicit and implicit biases respectively. Table 2 shows the detailed descriptive statistics for the scores obtained both pre- and post-intervention. Table 2 Descriptive statistics Pre PPMI Pre IAT Post PPMI Post IAT N Valid 408 408 408 408 Missing 0 0 0 0 Mean .034 .509 − .195 .488 Std. Error of Mean .028 .023 .028 .024 Median − .071 .301 − .178 .291 Std. Deviation .558 .474 .575 .475 Range 2.000 1.925 2.393 2.092 4.1.1 Correlation analysis The Spearman correlation analysis was conducted to examine the relationship between the PPMI and IAT scores. The analysis denotes a strong positive correlation between the two measures r s (408) = .65, p < .001, suggesting that higher explicit prejudice scores were associated with higher implicit bias scores. This correlation is further illustrated by the scatter plot in Fig. 2 , which demonstrates the linear relationship between explicit and implicit biases. 4.1.2 Group comparisons History of Mental Illness. The Mann-Whitney U test was employed to determine whether PPMI and IAT scores differed between participants with and without a history of mental illness. The results indicated that individuals with a history of mental illness exhibited significantly lower scores on both PPMI and IAT respectively, \(\:(z=-14.667;-14.582,p<0.001)\) compared to those without such a history. The mean rank of PPMI score for people with prior history of mental illness was 91.29, as opposed to 252.41 for those without history. Also, the same for IAT was 91.77 compared to 252.01. Educational Background. The Kruskal Wallis test showed there was a significant difference in PPMI and IAT scores across different types of qualifications attained, \(\:\chi\:2\:(4,\:408=48.781),\:p<0.001\) ; \(\:\chi\:2\left(4,\:408=50.031\right),p<0.001\) respectively. Figure 3 represents the boxplot of PPMI and IAT scores across different qualification backgrounds, illustrating the distribution and variability of scores and highlighting the differences between the groups as assessed by this test. The mean ranks of PPMI scores for people were 116.30 with medical degree, 216.16 for bachelor’s degree, 225.61 for high school, 221.79 for some college/university degree or vocational training and 230.14 for master’s degree. On the other hand, the mean ranks of IAT scores for people were 115.20 with medical degree, 217.89 for bachelor’s degree, 222.54 for high school, 221.23 for some college/university degree or vocational training and 232.68 for master’s degree. The post-hoc comparisons using Dunn’s method with a Bonferroni correction for multiple tests indicated that the PPMI and IAT scores of individuals with medical degree were significantly lower than that of people with other qualification degrees, \(\:p<0.001\) in all pair of comparisons with respect to medical degree. 4.2 Phase 2 The analysis for this phase of the study commenced with Levene's Test to assess the homogeneity of variances across intervention groups for variables including Age, Gender, Highest Qualification, Occupation, History of Mental Illness, Experiences with Mental Health Services, and post-intervention scores of both PPMI and IAT. The results indicated that variances for all characteristics were homogeneous across the intervention groups, \(\:p>0.05\) for all sets (see Appendix C). Given the confirmed non-normal distribution of the scores, non-parametric tests were again employed for further analysis. The Independent Samples Kruskal Wallis test among pre-intervention scores across all experimental groups showed no significant difference in the distribution of both scores across all groups, \(\:\chi\:2\:\left(3,\:\:408\right)=1.961,\:p=.580\) for PPMI scores and \(\:\chi\:2\:\left(3,\:\:408\right)=1.555,p=.670\) for IAT scores. 4.2.1 Intervention efficacy Between-Group Comparisons. The Kruskal Wallis test assessed the difference scores across the four experimental groups: Direct Contact, Indirect Contact, Education Empowerment, and Control. The results indicated significant differences in prejudice levels among the groups \(\:\chi\:2\:\left(3,\:\:408\right)=362.849,\:p<0.001\) for PPMI_Diff and \(\:\chi\:2\:\left(3,\:\:408\right)=341.135,\:p<0.001\) for IAT_Diff. The mean ranks of PPMI_Diff scores were 55.54 for Direct Contact, 156.45 for Indirect Contact, 251.48 for Education Empowerment Group and 354.53 for Control Group. Meanwhile, the same for IAT_Diff scores were 58.54 for Direct Contact, 172.22 for Indirect Contact, 229.75 for Education Empowerment Group and 357.50 for Control Group. The post-hoc comparisons using Dunn’s method with a Bonferroni correction for multiple tests showed that the difference PPMI and IAT scores of Control group was significantly higher than other groups (see Table 3 and Table 4 ). Table 3 Pairwise Comparisons of difference PPMI Scores Across Intervention Groups Sample 1-Sample 2 Test Statistic Std. Error Std. Test Statistic Sig. Adj. Sig. a Direct contact-Indirect Contact -100.907 16.470 -6.127 .000 .000 Direct contact-Education empowerment 195.931 16.470 11.896 .000 .000 Direct contact-Control group -298.985 16.470 -18.154 .000 .000 Indirect Contact-Education empowerment 95.025 16.470 5.770 .000 .000 Indirect Contact-Control group -198.078 16.470 -12.027 .000 .000 Education empowerment-Control group -103.054 16.470 -6.257 .000 .000 Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances (2-sided tests) are displayed. The significance level is .05. Note a Significance values have been adjusted by the Bonferroni correction for multiple tests. Table 4 Pairwise Comparisons of difference IAT Scores Across Intervention Groups Sample 1-Sample 2 Test Statistic Std. Error Std. Test Statistic Sig. Adj. Sig. a Direct contact-Indirect Contact -113.676 16.492 -6.893 .000 .000 Direct contact-Education empowerment 195.931 16.492 10.381 .000 .000 Direct contact-Control group -298.961 16.492 -18.127 .000 .000 Indirect Contact-Education empowerment 57.592 16.492 3.488 .000 .000 Indirect Contact-Control group -185.284 16.492 -11.235 .000 .000 Education empowerment-Control group -127.755 16.492 -7.746 .000 .000 Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances (2-sided tests) are displayed. The significance level is .05. Note a Significance values have been adjusted by the Bonferroni correction for multiple tests. Within-Group Comparisons. A Wilcoxon Signed Rank test comparing pre- and post-intervention scores within all groups indicated that all experimental groups showed significant differences in both PPMI and IAT scores. However, there was no significant difference for the Control group (Z = -1.102, p = .270 for PPMI; Z = -1.908, p = .065 for IAT), confirming that the observed changes. Specifically, the mean pre-intervention PPMI score for the Control group was 0.036 (SD = 0.527), which remained barely changed post-intervention (mean = 0.045, SD = 0.526). Similarly, the mean pre-intervention IAT score 0.485 (SD = 0.456), also remained unchanged post-intervention (mean = 0.485, SD = 0.456). 5. Discussion The paper aimed to elucidate the interaction between explicit and implicit prejudices in provoking discriminatory attitudes and behaviors towards people with mental health conditions. It also sought to experimentally analyze the effectiveness of various interventions in mitigating those prejudicial attitudes. The results of the study are discussed in the context of the hypotheses formulated, evaluates the significance of the findings, compares them with previous research, and explores their implications for the field of psychology. 5.1 Hypothesis 1 It proposed that a significant correlation between explicit and implicit biases against mental illness existed. The findings from the Spearman correlation analysis support this hypothesis, indicating a strong positive correlation between PPMI and IAT scores. This suggests that individuals who exhibit higher levels of explicit prejudice also tend to harbor stronger implicit biases and vice versa. These results are consistent with previous research that has found significant correlations between explicit and implicit measures of prejudice across various domains, including racial biases and attitudes toward different social groups (González-Sanguino et al., 2019 ; Stier & Hinshaw, 2007 ). However, those reports contained lesser values of rho than this result. Probably, because of distortions in the response tendencies and social desirability in assessing explicit prejudices. Yet, the strong correlation observed in this study aligns with the Dual-Process Theory, which posits that implicit and explicit attitudes can influence behavior in different yet interconnected ways (Kahneman, 2011 ). This theory suggests that while explicit attitudes are consciously controlled and can be modified through direct interventions, implicit attitudes operate automatically and may require different strategies for change. The strong correlation between scores reveals that mental illness stigma is deeply ingrained, affecting both conscious and unconscious attitudes. This finding underscores the critical need to address both explicit and implicit biases, as focusing on only one may be insufficient to ameliorate them properly. It also highlights the importance of developing integrated interventions that can simultaneously combat both forms of prejudice to effectively reduce stigma. By understanding this dual pathway through which stigma operates, mental health professionals and policymakers can develop more effective strategies to combat stigma and promote more accepting and supportive attitudes toward individuals with mental illness. 5.2 Hypothesis 2 It posited that individuals with a history of mental illness would exhibit significantly lower scores on both PPMI and IAT scores compared to those without such a history. The significantly lower scores of individuals with a history of mental illness illuminate the profound impact of personal experience on reducing stigma. This outcome suggests that those who have personally navigated the challenges of mental illness develop more empathetic and less prejudicial attitudes, likely stemming from a deeper understanding and awareness of the complexities associated with mental health conditions. Such findings are consistent with existing research, which often underscores that firsthand experience can significantly diminish negative biases and foster more supportive perspectives. Moreover, these results underscore critical implications for stigma reduction strategies (Maunder & White, 2019 ; Adu et. al., 2021 ). They highlight the potential efficacy of interventions that facilitate the sharing of personal experiences with mental illness. Educational programs and anti-stigma campaigns that incorporate narratives or testimonials from individuals who have lived through mental health challenges can effectively diminish both forms of biases. This approach is well-aligned with the Contact Theory, which asserts that interpersonal interactions with stigmatized groups can significantly reduce prejudices. In essence, this observed lower stigma scores emphasize the transformative power of personal experience in combating stigma (Allport, 1954 ). By integrating personal stories and lived experiences into intervention strategies, mental health professionals can create more compelling and relatable content that resonates with a wider audience. This method can play a crucial role in fostering greater acceptance and understanding of mental health issues, thereby contributing to a more inclusive and supportive societal attitude toward mental illness. 5.3 Hypothesis 3 It stated that participants with a medical education background would have significantly lower scores on both tests than those from other educational backgrounds. The widely held notion that specialized knowledge and training can mitigate stigmatizing attitudes came true through this result which indeed showed significantly lower scores of participants from medical education background. This result suggests that medical education, which typically includes comprehensive information about mental health disorders, their causes, and treatment options, alongside the clinical rotations within psychiatry during their training can effectively reduce both explicit and implicit biases against mental illness. Such comprehensive theoretical and practical exposure likely fosters a more nuanced and empathetic understanding of mental health issues, thereby diminishing prejudicial attitudes. These results resonate with previous studies that have demonstrated the efficacy of medical education in reducing mental health stigma (De Witt et. al., 2019 ; Papish et. al., 2013 ). Theoretical perspectives such as the Dual-Process Theory provide further insight into these findings. Medical education, with its detailed and systematic approach to mental health, likely influences both pathways of Dual-Process theory, System 1 and System 2 , promoting more informed and less biased attitudes. Moreover, the significant differences observed between medical and non-medical participants underscore the importance of targeted educational interventions. For instance, integrating mental health education into broader educational curricula could be a powerful tool in reducing stigma across diverse populations. By providing individuals with accurate information and fostering a deeper understanding of mental health issues, such educational initiatives can substantially contribute to reducing prejudicial attitudes and promoting a more supportive and inclusive attitude towards individuals with mental illness. 5.4 Hypothesis 4 The implications of previously examined hypotheses led to designing an experiment that first proposed possible interventions and later analyzed their relative effectiveness towards combating prejudices. The current hypothesis focused on evaluating the efficacy of a direct contact intervention program on reducing prejudice, compared to control and other alternative intervention groups. The results provided robust evidence supporting the hypothesis that individuals undergoing direct contact with individual who had navigated all the challenges including onset, prognosis and stigma would demonstrate a significantly greater reduction in prejudice scores. The thorough application of various statistical tests provided a solid foundation for hypothesis testing and ensured the robustness of the study’s conclusions. The homogeneity as evidenced by Levene’s test with all variables, insignificant different score of control group as shown via Related Samples Wilcoxon-Signed Rank test between pre-and post-intervention test scores and significant difference in pre-intervention test scores across all groups as proven by Independent-Samples Kruskal Wallis test collectively concluded that the significance of Independent Samples Kruskal Wallis test of post-intervention test scores across all groups was indeed the result of intervention programs ruling out all possible variables that would have impacted the study otherwise. This along with Post hoc Dunn's test elucidated that the direct contact intervention group experienced a significantly greatest reduction in prejudice scores compared to both the control and alternative intervention groups. Specifically, the largest negative z-values observed in the comparison between the direct contact and control groups (-298.99 for PPMI and − 298.96 for IAT) highlight the substantial effectiveness of direct contact in reducing prejudice. Similarly, the indirect contact group also demonstrated a significant reduction in prejudice scores, though less pronounced than the direct contact group (z = -198.078 for PPMI and z = -185.28 for IAT) compared to the control group. The education empowerment group, while showing the smallest impact, still achieved a notable reduction in prejudice scores relative to the control group (z = -103.054 for PPMI and z = -127.76 for IAT). These findings suggest a gradient of effectiveness, with the direct contact intervention being the most effective, followed by the indirect contact group, and then the education empowerment group. The Direct Contact group's effectiveness can be attributed to the unique advantages it offers in humanizing individuals with mental illness. This finding aligns with Contact theory, which posits that direct interaction with members of stigmatized groups can reduce prejudice by fostering empathy and breaking down stereotypes. The result is also consistent with other similar studies; for example, Atienza-Carbonell et. al. ( 2022 ) conducted an experiment with medical students, where interactions with patients who also served as educators led to a significant reduction in stigma. The personal stories and direct experiences shared in our group provided participants with a deeper, more relatable understanding of mental illness, which is often lacking in more impersonal forms of education. In comparison, the indirect contact group, which engaged with the film Taare Zameen Par , also showed significant reductions in stigma, The film, through its depiction of a young boy's struggles with dyslexia, offered a powerful narrative that elicited empathy and reflection. This also aligns with previous research suggesting that media portrayals can effectively reduce stigma by providing relatable and emotional stories. In contrast, Poulgrain et. al ( 2022 ) in their recent study found increment in PPMI scores of participants who watched a film Joker , movie depicting the dangerousness of a mentally ill person. Meanwhile, the education empowerment group, which focused on providing factual information and raising awareness about mental health issues, showed some reduction in stigma but to the least extent among the groups. This suggests that while knowledge and awareness are essential components of stigma reduction, they may not be as effective in isolation. Educational interventions might lack the emotional engagement necessary to deeply challenge and change prejudicial attitudes, highlighting the need for integrated approaches that combine information with personal narratives or interactions. Besides, the superior efficacy of direct contact interventions suggests prioritizing experiential learning in anti-stigma efforts. Given the relative effectiveness observed, combining contact in addition to education empowerment programs could potentially enhance the overall impact on prejudice reduction. This combined approach can leverage the strengths of both contact-based interaction and educational empowerment, offering a more comprehensive strategy to address and reduce prejudice. These insights collectively inform policymakers, educators, and mental health professionals about effective strategies to foster more inclusive and supportive attitudes towards individuals with mental illness, thereby contributing to more empathetic and informed public health initiatives. 6. Limitations and future recommendations Despite the promising results, this study has few limitations that should be acknowledged. One significant limitation arises from the use of convenience and snowball sampling, which can introduce sample bias, potentially hindering the generalizability of findings. Additionally, the reliance on self-report measures for assessing prejudice may be subject to social desirability bias. While the study focused on short-term impacts of the interventions focusing on specific temporal snapshot, long-term follow-up studies are needed to assess the sustainability of the observed changes in attitudes. Also, the experimental phase designed to investigate cognitive mechanisms, is subject to constraints stemming from its artificial, controlled nature, potentially affecting the ecological validity of results. Building on the current study, examining the specific components of the direct contact intervention that contribute most significantly to prejudice reduction could inform the development of more targeted and effective programs. Further research should also explore the differential impact of these interventions across diverse demographic groups. Understanding how factors such as age, gender, socioeconomic status, and cultural background influence the effectiveness of anti-stigma interventions can help in tailoring programs to better meet the needs of specific populations. Additionally, future research could investigate the synergistic effects of combining direct contact with educational empowerment to enhance the efficacy of prejudice reduction efforts. 7. Conclusion This research provides valuable insights into the mechanisms driving mental illness stigma and contributes significantly to the understanding of how different interventions can reduce prejudice. The findings advocate for the implementation of contact-based programs and suggest that combining them with educational strategies could enhance their effectiveness. The implications of these findings extend to policymakers, educators, and mental health professionals, offering a roadmap for developing more inclusive and supportive environments for individuals with mental illness. By addressing both explicit and implicit biases through comprehensive and targeted interventions, we can foster a society that embraces mental health with empathy and understanding. Glossary Attitude: A relatively enduring and general evaluation of an object, person, group, issue, or concept on a dimension ranging from negative to positive (APA, 2018). For example, Ali has positive attitudes towards environmental conservation; he always recycles, uses public transportation, and advocates for green policies. Bias: An inclination or predisposition for or against something (APA, 2018). For example, the hiring manager's preference [bias] toward applicants from prestigious universities led him to overlook qualified candidates from lesser-known schools. Prejudice: A negative evaluation characterized by cognitive and affective responses that subsequently trigger discriminatory behavior (Görzig & Ryan, 2022). For instance, despite never having met anyone from the country, Sarah held a prejudice [negative evaluation] against people from that region, believing they were all unfriendly and untrustworthy. Stereotype: A cognitive structure fixed and over-generalized that divide people into groups or categories (Corrigan, 2018). For instance, the widely held image [stereotype] that all elderly people are bad with technology is unfair and inaccurate, as many seniors are quite proficient with smartphones and computers. Stigma: The social devaluation of individuals and groups based on attributes they possess that mark them as discredited along specific axes of social desirability (Goffman, 2009). For example, the stigma around mental illness prevents people from seeking help they need due to the fear of judgement and discrimination. Declarations Acknowledgements The author extends deepest gratitude to Bela Khan for her unwavering guidance and mentorship throughout every facet of this academic journey. Her expertise and support have been crucial in the author's development as a scholar. The author is also profoundly grateful to Sunaina Awasthi, who candidly shared her experiences to one of the experimental groups. Her valuable insights on combating mental illness stigma and the challenges in treatment have not only enriched this study but also provided a powerful testament to resilience and hope for many. Author contributions: Credit The author solely contributed to all aspects of the research, including conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft preparation, and editing, visualization, supervision, project administration, and so on. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Ethics Declaration and Human Participation Consent Ensuring ethical integrity in research is paramount, especially when dealing with sensitive topics like mental health. The current study adhered strictly to ethical guidelines approved by the Institutional Review Board at International Open University, to protect participants' rights, dignity, and well-being. Comprehensive informed consent was obtained, ensuring participants were aware of the study's purpose, procedures, potential risks, benefits, and their right to withdraw at any time without penalty. Confidentiality was rigorously maintained by anonymizing any identifying information and securely storing data. Participants were thoroughly debriefed at the study's commencement, with access to mental health support resources provided throughout the study. The study protocol was duly signed and approved by the concerned body, ensuring compliance with ethical standards for human subject research. Efforts were made to minimize harm, including providing support resources to participants in the direct contact group. Participation was entirely voluntary, with no coercion involved, particularly in the convenience and snowball sampling methods used. Cultural sensitivity was emphasized, with research tools and interventions adapted to the Nepalese context to ensure relevance and respect for participants' cultural backgrounds and beliefs. The study also included regular check-ins with participants to monitor their well-being and offered additional support if needed. By adhering to these ethical principles, the study safeguarded participant well-being and upheld research integrity. Declaration of generative AI and AI-assisted technologies The author used ChatGPT, a generative AI model, to assist in paraphrasing certain sections of this manuscript to enhance its readability and linguistic quality. 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Assessing the belief that the behavior of individuals with mental illness is unpredictable. Capturing beliefs in the need to coercively control individuals with mental illness. Reflecting beliefs in the inferiority of individuals with mental illness and a lack of sympathy for them. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5014975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352270180,"identity":"607504e9-6e58-4b62-a9ec-06daec3bd3c2","order_by":0,"name":"Mohammad Irfan Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYFACHgaJBAY5IM0D5FTYyPGDBBMKCGoxhmo5k2Ys2QDSYkBACwNMC2Pb4USDAyBRPFp023sP3ni4x0DenOfswc8FbGkJxudXJ354YMAgzy92AKsWszPnki0SnhkY7uztS5aewWOTZ3bj7WYJoMMMZ85OwK7lRo6ZRMKBP4wbzvMYSPNIpBWb3Ti7AaQlweA2Xi0G9kAtxr95DA4nbp5xdvMPYrQkbjjbYybNk3A4cQN/7zb8tpw5Y2wB1JK84cwZM2ueA2nGEjd4t1kkGEjg9svxHsObPw4Y2G44k2N8m/cfMCr7z26++aPCRp5fGrsWLEACrFKCWOUgwH+AFNWjYBSMglEwAgAARwlk5JXBnmcAAAAASUVORK5CYII=","orcid":"","institution":"International Open University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Irfan","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-09-02 02:52:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5014975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5014975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66950602,"identity":"bae3df25-e980-4738-86c4-06574670fea7","added_by":"auto","created_at":"2024-10-18 10:15:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":541766,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework for Understanding \u0026amp; Mitigating Mental Health Prejudices\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5014975/v1/9a9374b1208b4d3b0b6c8fdb.png"},{"id":66950603,"identity":"ab993e51-7215-4555-959a-7e342bad2845","added_by":"auto","created_at":"2024-10-18 10:15:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSimple Scatter plot of IAT by PPMI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5014975/v1/8e5c35c887de6184b6fd6fe1.png"},{"id":66950605,"identity":"624e3f99-53af-43c2-bdc6-8a29bd82847e","added_by":"auto","created_at":"2024-10-18 10:15:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183458,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of Scores by Group from Krushkal Wallis Test\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5014975/v1/409c7119a8b826c8fb15aad9.png"},{"id":67686887,"identity":"da4bc45c-3e9a-42b9-b676-ebb53203f1c0","added_by":"auto","created_at":"2024-10-28 16:47:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1669224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5014975/v1/824ce83b-e45c-4a9f-b637-a5d4fe3b2e25.pdf"},{"id":66950604,"identity":"079f5ec2-dc1b-4874-ac44-8c7e5daaadf8","added_by":"auto","created_at":"2024-10-18 10:15:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30784,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5014975/v1/3fb351e84faa64ed896040ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding and Addressing Prejudices Faced by Mentally Ill Individuals: A Multidimensional Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUnlike other illnesses, mental illness is not a monolithic concept but rather a complex and multifaceted phenomenon encompassing a wide array of psychological, emotional and behavioral disorders. Probably, the ramification of this inherent complexity often leads to misconceptions and biases; thereby, contributing to the stigmatization and discrimination of individuals and their families with mental health conditions. Despite advancements in our understanding of mental illness and overwhelming anti-stigma maneuvers over the past few decades, misconceptions, biases, and discriminatory attitudes continue to hinder the well-being and societal integration of those affected. This study endeavors to delve into the multifaceted nature of these prejudices faced by mentally ill individuals, aiming not only to comprehend the root causes but also to propose effective strategies for their mitigation. The study\u0026rsquo;s focus on identifying sustainable interventions offers valuable insights for policymakers, healthcare providers, and educators. Ultimately, this research has the potential to significantly enhance the quality of life for individuals with mental health conditions by promoting a deeper understanding and reducing the societal barriers they face.\u003c/p\u003e \u003cp\u003eHistorically, mental illness has been misunderstood and feared, with misconceptions shaping societal attitudes and responses over time. In many ancient cultures, mental illness was often attributed to supernatural forces or the influence of spirits and demons; for example, the ancient Greeks and Romans sometimes saw it as a form of divine punishment or demonic possession (Fabrega, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Similarly, during the Middle Ages in Europe, mental illness was frequently viewed through a religious lens, with many attributing such conditions to witchcraft or possession by evil spirits. Throughout various periods, particularly in Western societies, mental illness was also sometimes perceived as stemming from moral or character flaws, a perspective that persisted into the 18th and 19th centuries, where societal and moral judgments were often placed on individuals exhibiting symptoms of mental health disorders (p. 294). These archaic views have evolved, yet remnants of these misconceptions persist in modern societies. For example, mentally ill individuals in Nepalese society are ubiquitously labelled as \u0026lsquo;\u003cem\u003ealter\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003ecrazy\u003c/em\u003e\u0026rsquo;, or \u0026lsquo;\u003cem\u003emad\u003c/em\u003e\u0026rsquo;.\u003c/p\u003e \u003cp\u003eStigmatizing attitudes pervade nearly every aspect of life for those affected by mental illness (Kenny \u0026amp; Bizumic, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In healthcare settings, individuals with mental health conditions often face unwarranted judgments, leading to misdiagnoses or inadequate treatment (Bizumic et. al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, healthcare providers may inadvertently attribute physical symptoms to the mental illness itself rather than thoroughly investigating potential underlying physical health conditions, resulting in meagre care (Kolb et. al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the realm of employment, individuals may encounter discrimination that restricts job opportunities and impedes career progression (Young et. al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Employers may be reluctant to hire or promote individuals with mental health issues due to baseless concerns about their reliability or productivity, leading to underemployment and financial instability (Balogun-Mwangi, 2023). Within educational settings, students with mental health issues might be unjustly labelled or receive insufficient support, impeding their academic progress. Educators and administrators often lack the necessary training to identify and accommodate mental health needs, resulting in a dearth of appropriate interventions and support services (Reavley et. al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Interpersonal relationships are also strained by stigma, affecting interactions with family, friends, and colleagues (Yates \u0026amp; Gatsou, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Loved ones may distance themselves due to misconceptions about mental illness, and colleagues might avoid collaborating closely with someone known to have a mental health condition, exacerbating social isolation and weakening support networks (Fox et. al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The most detrimental consequence of these stigmas is their exacerbation of the illness's severity, often overshadowing the primary symptoms of the disorder itself. Experienced stigma, characterized by direct discrimination and negative attitudes from others, leads to social isolation, reduced access to healthcare, and diminished opportunities in employment, education, and social interactions (Evans et. al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This form of stigma can result in individuals feeling marginalized and alienated, further exacerbating mental health symptoms and impeding recovery. Anticipated stigma, or the fear of being stigmatized, can deter individuals from seeking essential treatment and support due to concerns about being judged or discriminated against (Adu et. al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This apprehension can prevent individuals from accessing early intervention and continuous care, which are crucial for effectively managing mental health conditions. Affiliate stigma, experienced by family members and close associates of individuals with mental condition, further complicates the issue (Li et. al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These individuals often face social rejection and prejudice due to their association with someone with a mental health condition, which can lead to additional stress and emotional burden on the support network, thereby indirectly impacting the person with the illness (Shahwan et. al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Self-stigma, wherein individuals internalize societal prejudices, further compounds the issue by diminishing self-esteem and self-efficacy (Gonz\u0026aacute;lez-Sanguino et. al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Internalized stigma induces feelings of shame, guilt, and worthlessness, making individuals less likely to seek help and more likely to withdraw from social interactions (Arboleya-Faedo et. al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This internal conflict can further lead individuals to undermine their own illness or treatment by avoiding professional help, failing to adhere to prescribed regimens, or discontinuing treatment prematurely (Schomerus et. al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the prognosis of such disorders due to aforementioned dimensions of stigma becomes severely compromised further cascading into severe mental illness which could have been easily ameliorated otherwise.\u003c/p\u003e \u003cp\u003eA wide body of literature has been dedicated to the conceptualization and measurement of mental illness stigma. Various methodologies have been employed, including qualitative studies, surveys, and experimental designs. Qualitative studies provide in-depth insights into personal experiences of stigma, capturing the nuanced ways in which individuals perceive and are affected by stigma (FitzGerald et., al., 2019; Maunder \u0026amp; White, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Thornicroft, et. al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Surveys offer broad data on the prevalence and impact of stigma across different populations, enabling researchers to identify widespread patterns and correlations (Sheppard et. al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bayındır et. al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fung et. al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Poulgrain et. al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fang et. al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Klik et. al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Experimental designs have been used to test interventions aimed at reducing stigma, providing empirical evidence on the effectiveness of different strategies (Cho \u0026amp; Kim, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Atienza-Carbonell, 2022; G\u0026ouml;rzig \u0026amp; Ryan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Subramanian \u0026amp; Santo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brown \u0026amp; Russel, 2019; De Witt et. al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, despite the substantial focus on stigma research, there remains a significant gap in literature. Many studies are limited to a single agenda, either focusing exclusively on measurement, conceptualization, or interventions, rather than providing a comprehensive view that integrates these aspects. Moreover, such studies are often lacking the integration of explicit and implicit prejudices in their measurement, conceptualization, and intervention processes. Explicit biases can be moderated by participants during research, thereby obscuring underlying evaluations. Meanwhile, the prevalence of implicit biases alone does not always lead to provoking discriminatory behavior. Therefore, it is equally important to incorporate implicit measures to avoid the influence of control-related responses in explicit measures and understand what extent of underlying implicit biases result in expression of detrimental behaviors towards individuals with mental health conditions. The current research study was of paramount importance as it aimed to fill this wide gap. In addition, by experimentally developing and evaluating strategies to effectively reduce both forms of stigma, this research aimed to improve mental health outcomes and foster a more inclusive society thereafter. Accordingly, this study posited several key hypotheses aimed at understanding and addressing mental health stigma:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sub\u003e A statistically significant positive correlation exists between PPMI and IAT scores across diverse demographic groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e People with a history of mental illness are anticipated to demonstrate significantly lower scores on both the PPMI and IAT measures compared to those without such a history.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e3\u003c/b\u003e \u003c/sub\u003e Participants with a medical education background are expected to exhibit significantly lower scores on both the PPMI scale and the IAT in contrast to individuals from other educational backgrounds.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH\u003c/b\u003e \u003csub\u003e \u003cb\u003e4\u003c/b\u003e \u003c/sub\u003e Individuals who undergo a direct contact intervention program are expected to demonstrate a significantly greater reduction in scores on both the PPMI and IAT measures compared to those in control and alternative intervention groups.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThe focus of this study was to elucidate the complex interplay of explicit and implicit prejudices in maintaining and perpetuating stigmas against people with mental health conditions and determine the most efficacious strategies to ameliorate those attitudes; therefore, this research unfolded in two distinct phases to provide a comprehensive understanding and evaluate effective interventions to combat stigma. \u003cem\u003ePhase 1\u003c/em\u003e commenced by quantifying explicit prejudices through the PPMI scale (Kenny et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), succeeded by the assessment of implicit biases using the Mental Illness IAT (Borchert, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This dual quantitative approach endeavored to holistically grasp the intricate interplay of explicit and implicit biases that fuel the perpetuation and maintenance of stigma against individuals wrestling with mental disorders. Also, these initial assessments provided crucial data points for comparing changes in prejudices across all groups. The study then pivoted in \u003cem\u003ePhase 2\u003c/em\u003e, towards experimentally pinpointing efficacious interventions to combat such stigmas. The same participants were randomly assigned to one of four distinct groups based on the type of interventions they receive: direct contact, indirect contact, educational empowerment, or a control condition. Each intervention cohort then engaged in a structured regimen, followed by re-administration of both tests after two weeks to gauge the transformative impact of the interventions. Randomization ensured the impartial allocation of participants and the equal distribution of any unknown extraneous variables across intervention groups, while control group served as a baseline comparison tool. Additionally, demographic variables were controlled to account for potential confounding factors.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Direct Contact\u003c/h2\u003e \u003cp\u003eParticipants in this group interacted with an individual who had personally navigated the challenges of major depression with suicidal ideation. Over the course of two weeks, she candidly shared her journey, providing an in-depth narrative that covered the onset and progression of her symptoms, the profound impact of her diagnosis, and her three-month stay in a psychiatric ward. This comprehensive account included her experiences with various treatment modalities such as pharmacological interventions, counseling, and therapeutic approaches, including TMS[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. She delved into the intricacies of living with mental illness, highlighting the daily battles she faced, not only with the illness itself but also with the pervasive stigma that often accompanies such diagnoses. By sharing her personal struggles and triumphs, she offered a raw and authentic perspective on the realities of mental health conditions.\u003c/p\u003e \u003cp\u003eIn addition to recounting her personal experiences, she discussed her role as an advocate for mental health awareness. She explained how her journey inspired her to write the novel, \u003cem\u003eSanely Insane\u003c/em\u003e (Awasthi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which serves as a powerful tool to combat mental illness stigma. The novel sheds light on common myths and misconceptions about mental illness and emphasizes that, contrary to popular belief, most cases are treatable with the right interventions. Participants in this group could engage in meaningful dialogues with her, asking questions and discussing their own perceptions and biases. This group was designed to humanize the experience of mental illness and foster empathy and understanding, challenging the preconceived notions that often contribute to stigma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Indirect contact\u003c/h2\u003e \u003cp\u003eNot everyone has the chance to engage in real-life interactions with individuals experiencing mental health challenges; therefore, virtual contact serves as an effective alternative. Participants in this group engaged with the film \u003cem\u003eTaare Zameen Par\u003c/em\u003e[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e [Like Stars on Earth] (Khan, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The movie portrays the journey of a young boy grappling with dyslexia, offering a poignant depiction of the struggles including stigma, endured by individuals contending with mental health challenges and the transformative influence of supportive interventions. It was presented in seven carefully selected segments over a two-week period, each highlighting unique themes, allowing participants to reflect on the experiences depicted in the film and draw parallels to real-world attitudes towards mental health (see Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eA1\u003c/span\u003e). Participants engaged with each segment on alternate days, affording them opportunities for contemplation and group dialogue the subsequent day. For instance, following the viewing of segment 1 on day 1, participants convened for a collective discussion on day 2 before proceeding with the subsequent segment on day 3, continuing for 14 days. This iterative approach was designed to provide participants with a profound understanding of mental health stigma and the impact of supportive environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Education empowerment\u003c/h2\u003e \u003cp\u003eParticipants in this group engaged in a comprehensive series of structured educational sessions, meticulously designed to enhance their understanding of mental health issues over a two-week period. These sessions were thoughtfully curated to cover a broad spectrum of topics, providing a holistic and in-depth exploration of mental health. The program began with foundational knowledge, aiming to establish a robust understanding of mental health conditions. During the first week, the group delved into the nature and classification of various mental health disorders, discussing their symptoms, causes, and prevalence. This included an exploration of the biological, psychological, and social factors contributing to mental illnesses. Participants also learned about the latest research findings and the scientific basis for different treatment approaches, including pharmacological interventions, psychotherapy, and emerging therapeutic modalities such as brain stimulation techniques, mindfulness, and exercise. Meanwhile, they explored the social dimensions of mental health, discussing the pervasive effects of stigma, the critical role of social support, and effective strategies for advocacy and empowerment in the second week. Participants were also encouraged to reflect on their attitudes and beliefs about mental illness, share personal experiences, and actively participate in a supportive and empathetic learning environment. These sessions, in both weeks, were thoughtfully designed to be highly interactive, incorporating group activities and discussions to foster engagement and enhance information retention. Through these interactive sessions, common myths and misconceptions about mental health were debunked, effectively reducing stigma and fostering empathy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Control group\u003c/h2\u003e \u003cp\u003eParticipants in the control group did not receive any specific intervention during the two-week period. Instead, they continued with their usual routines without exposure to the structured sessions or educational content provided to the experimental groups. However, they were subject to the same assessment procedures as the other groups. Initially, they completed both tests to establish their baseline levels of prejudices. Throughout the two-week period, the group was monitored to ensure they were not inadvertently exposed to any intervention-related content. This monitoring helped to maintain the integrity of the control condition by preventing any external influences that could affect their scores. The inclusion of the control group is critical for isolating the effects of the specific interventions on reducing mental health stigma. By comparing the changes in prejudices between the control and experimental groups, the study could attribute any observed differences to the impact of the interventions rather than to external factors or natural variations over time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants and procedure\u003c/h2\u003e \u003cp\u003eThe current research was carried out in Nepal, a south-east Asian country with diverse population. A priori power analysis using G*Power V3.1.9.7 was performed to determine the necessary sample size (Faul et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A minimum sample of 279 was required to detect a medium effect size with 80% power for ANCOVA with 4 distinct groups and a covariate. The recruitment campaign encouraging potential participants was circulated on social media platforms, online forums, and mental health organizations targeting the diverse Nepalese community to join. It highlighted the study's aims and the importance of understanding and addressing mental health stigma. Initially, an attempt was made to recruit participants randomly; however, due to the stigma associated with mental health, this approach yielded a limited pool of participants. Consequently, convenience sampling was employed, targeting individuals readily accessible to the researcher. Additionally, word-of-mouth referrals, where initial participants were encouraged to refer friends and family members. This method facilitated the recruitment of a broader and more varied sample, enhancing the representativeness and generalizability of the study findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measures\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Demography\u003c/h2\u003e \u003cp\u003eInitially, a substantial number of individuals (\u003cem\u003en\u0026thinsp;=\u0026thinsp;437\u003c/em\u003e) responded to participate in the research. However, following a thorough debriefing session, a few (\u003cem\u003en\u0026thinsp;=\u0026thinsp;29\u003c/em\u003e) chose to withdraw from the study. Remarkably, all participants who opted to proceed remained committed throughout the study's duration, providing comprehensive data across both phases. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a detailed summary of the sample demographics. Participants for this study were selected based on specific criteria to ensure the relevance and accuracy of the findings. All participants were Nepalese, aged between 18 and 60 years, and capable of providing informed consent. Inclusion criteria required participants to have no prior diagnosis of severe cognitive impairments or neurological disorders that could affect their ability to comprehend and complete the assessments. Additionally, individuals currently undergoing treatment for acute psychiatric conditions were excluded to prevent any interference with their ongoing therapies. The study aimed to encompass a diverse sample in terms of gender, socioeconomic status, and educational background, ensuring a broad representation of the Nepalese population. Participants were also required to have a basic understanding of mental health issues, either through personal experience or general awareness, to facilitate meaningful engagement with the study's interventions and assessments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSociodemographic Characteristics of Participants\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eNon-binary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest Qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college/university degree or vocational training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical degree (MBBS/BSc Nursing, MD/MS/MCh)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of Mental Illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience with Mental Health Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 PPMI\u003c/h2\u003e \u003cp\u003eThis scale is a widely used self-report questionnaire designed to measure explicit prejudices against individuals with mental health conditions. It has even been adjusted to measure specific mental illnesses such borderline personality disorder (Sheppard et. al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), schizophrenia and depression (Bizumic et. al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This scale comprises 28 items, each rated on a Likert scale ranging from \u0026minus;\u0026thinsp;2 (strongly disagree) to 2 (strongly agree). The PPMI Scale captures various dimensions of prejudice through its four subscales: Fear/Avoidance[3]\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e(8 items), Unpredictability[4]\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e(6 items), Authoritarianism[5]\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e(6 items), and Malevolence[6]\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e(8 items). The scale and its subscales are balanced with equal numbers of positively and negatively keyed items, avoiding double-barreled items. After reversing some scores as mentioned in the scale, the PPMI score was calculated by averaging the scores obtained in all 28 items. While the study used the overall PPMI scores to assess explicit prejudices, detailed analyses of the subscales were not the primary focus of this research. The PPMI Scale has demonstrated strong internal consistency and reliability across diverse populations. It has high construct validity, accurately measuring the concept of prejudice towards mental illness. Cronbach's alpha coefficients typically range from 0.85 to 0.92, indicating excellent reliability (Kenny et. al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Test-retest reliability over a two-week period has also been reported to be high, demonstrating stability over time (p. 11).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Mental illness IAT\u003c/h2\u003e \u003cp\u003eThe IAT is a widely used cognitive-behavioral paradigm that measures the strength of automatic [implicit] associations between concepts in people\u0026rsquo;s minds relying on latency measures in a simple sorting task (Greenwald et. al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The test involved a series of categorization tasks where participants quickly classify words and attributes related to two different pairings. The strength of an association between concepts is measured by the standardized mean difference (d-score) of the hypothesis-inconsistent and hypothesis-consistent pairings (Greenwald et.al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Mental Illness IAT is a computerized measure developed by Millisecond Software using this same principle to assesses implicit biases towards individuals with mental illness (Borchert, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Basically, the individuals with stronger implicit biases will be quicker to associate negative words with mental illness and positive words with physical. The validity of the IAT has been supported through numerous studies showing that IAT scores predict discriminatory behavior and attitudes. The reliability of the IAT is generally good, with internal consistency coefficients (split-half reliability) typically ranging from 0.7 to 0.9 (Jordan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its predictive validity, or the ability to predict relevant outcomes (e.g., behavior), further underscores its utility in psychological research.\u003c/p\u003e \u003cp\u003eBy employing these validated and standardized tools within this study's sample, the research ensured a comprehensive and reliable assessment of both explicit and implicit prejudices towards individuals with mental health conditions. These measurements were critical for understanding the baseline levels of stigma and evaluating the impact of the intervention programs on reducing mental health stigma.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Research methods and Variables\u003c/h2\u003e \u003cp\u003eThe methodology in this study predominantly relied on quantitative approaches, enabling a rigorous analysis of explicit and implicit biases and ensuring statistical robustness in assessing the effectiveness of stigma-reduction interventions. While qualitative methods were not explicitly employed, open-ended questions and group discussions were utilized in both contact groups (i.e., direct and indirect). However, these qualitative interactions were not subjected to formal analysis but rather served to enhance participant engagement and deepen their understanding of mental health stigma. The dependent variables encompassed explicit prejudices, measured through scores obtained from the PPMI Scale, and implicit biases, assessed via scores from the Mental Illness IAT. These variables provided insights into individuals' overt and unconscious biases towards mental health conditions, respectively. Meanwhile, the independent variables varied depending on the phase of the study itself. For instance, pre-intervention study incorporated demographic variables such as age, gender, education level, history of mental illness, experiences with mental health services and socioeconomic status as independent variables in contrast to the types of intervention included in post-intervention study. The observed changes in scores were calculated by subtracting the pre-intervention scores from the post-intervention scores; for example, \u003cem\u003ePPMI_Diff\u0026thinsp;=\u0026thinsp;Post_PPMI-Pre_PPMI\u003c/em\u003e and \u003cem\u003eIAT_Diff\u0026thinsp;=\u0026thinsp;Post_IAT-Pre_IAT\u003c/em\u003e. Overall, the meticulous control and operationalization of these variables facilitated a comprehensive understanding of mental health stigma and evaluated the efficacy of interventions in addressing this pervasive issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Analysis plan\u003c/h2\u003e \u003cp\u003eThe results of the research were collated, coded, and analyzed using IBM SPSS Statistics V25.0 (IBM Corporation, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Participants\u0026rsquo; characteristics were described using mean, standard deviation, frequency, and percentage (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Since all participants, after the debriefing process, volunteered until the end of the research, no data adjustment was required. The data analysis process commenced with checking for outliers and assessing the normality of the data distribution using visual inspection methods, including boxplots, histograms and Q-Q plots, alongside statistical tests such as the Kolmogorov-Smirnov and Shapiro-Wilk tests (see supplementary material). These analyses collectively confirmed that the data did not adhere to a normal distribution. Consequently, non-parametric statistical tests were employed for all analyses.\u003c/p\u003e \u003cp\u003eTo explore the relationship between explicit and implicit biases, Spearman correlation analysis was conducted examining the association between PPMI and IAT scores. The Mann-Whitney U test was utilized to investigate differences in both sets of scores based on participants' history of mental illness, facilitating comparisons between individuals with and without such a history. Additionally, the Kruskal Wallis test followed by Dunn\u0026rsquo;s test was employed to determine whether participants' educational background, specifically those with a medical education, yielded significantly different scores compared to participants from other educational backgrounds in Phase 1 of the study. In Phase 2, after implementing the intervention programs, the Kruskal Wallis test was employed to discern any significant differences in post-intervention scores across all four experimental groups. This was followed by post-hoc Dunn\u0026rsquo;s tests to identify the most effective intervention strategy in reducing stigma. Additionally, the Related-Samples Wilcoxon Signed-Rank test between pre- and post-intervention scores and Independent-Samples Kruskal Wallis test in pre-intervention scores across all groups were performed, alongside Levene\u0026rsquo;s test with all variables. These additional tests eliminated potential extraneous variables influencing post-intervention scores, thereby ensuring the accuracy of the inferences drawn from the experiment. All analyses were conducted separately for PPMI and IAT scores and at the significance level of \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e in this study unless mentioned in the test statistic otherwise.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Theory and calculation","content":"\u003cp\u003eThe theories that ground this study include the \u003cem\u003eDual-Process Theory\u003c/em\u003e (Tversky \u0026amp; Kahneman, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) and \u003cem\u003eContact Theory\u003c/em\u003e (Allport, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1954\u003c/span\u003e). Dual-Process theory posits that human cognition operates through two distinct pathways: an automatic, unconscious system called \u003cem\u003eimplicit\u003c/em\u003e and a controlled, conscious system called \u003cem\u003eexplicit\u003c/em\u003e. This theory was significantly advanced by Daniel Kahneman and his colleague Amos Tversky in the field of behavioral psychology, particularly through their work on cognitive biases and decision-making processes (Kahneman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tversky \u0026amp; Kahneman, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Their research highlighted how people often rely on heuristics, or mental shortcuts, which operate implicitly through \u003cem\u003eSystem 1\u003c/em\u003e. In contrast, \u003cem\u003eSystem 2\u003c/em\u003e involves more deliberate and reflective thinking processes, which require effort and conscious control (Kahneman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These insights were foundational in understanding how biases can co-operate implicitly and explicitly. The existing literature on Dual-Process theory has expanded to various domains, including social psychology, where it is used to explain the persistence of stereotypes and prejudices. Researchers like Banaji and Greenwald (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) have further explored implicit biases using IAT, demonstrating how subconscious associations can influence attitudes and behaviors even when individuals consciously endorse egalitarian beliefs. Contact theory, originally developed by Gordon Allport in 1954, posits that interpersonal contact under appropriate conditions can reduce prejudice between majority and minority group members. The theory suggests that prejudice can be reduced when individuals from different groups interact under conditions of equal status, common goals, cooperation, and institutional support (Allport, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1954\u003c/span\u003e). Subsequent research has supported and refined this theory, indicating that positive interactions with mentally ill individuals can reduce stigma and promote more favorable attitudes (Adu et. Al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maunder \u0026amp; White \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By leveraging these theories, the research aimed to elucidate how dual cognitive processes and interpersonal contact interact to perpetuate or mitigate stigmatizing attitudes and behaviors respectively. This framework not only facilitated a nuanced analysis of prejudice but also guides the development of targeted interventions that address both conscious biases and subconscious associations. Through this lens, the study sought to offer a robust, theoretically informed approach to dismantling stigma and promoting mental health equity. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of this framework.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eGiven non-adherence of collected data to normal distribution, non-parametric statistical tests were employed for subsequent analyses. These analyses provided a comprehensive overview of the factors influencing prejudice towards mental illness and the effectiveness of intervention strategies. The results are represented sequentially, reflecting the design of the study. The variables PPMI and IAT in subsequent sections denote the scores for their corresponding studies only. For example, PPMI within Phase 1 only denotes score for Phase 1.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Phase 1\u003c/h2\u003e \u003cp\u003e The study during this period focused on analyzing the baseline data collected from participants to understand the relationship between various demographic factors and prejudice towards mental illness, as well as to assess any existing correlations between explicit and implicit biases. The mean Pre PPMI score was 0.034 (SD\u0026thinsp;=\u0026thinsp;0.558), while the mean Pre IAT score was 0.509 (SD\u0026thinsp;=\u0026thinsp;0.474), indicating baseline levels of explicit and implicit biases respectively. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the detailed descriptive statistics for the scores obtained both pre- and post-intervention.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive statistics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre PPMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre IAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost PPMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePost IAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStd. Error of Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Correlation analysis\u003c/h2\u003e \u003cp\u003eThe Spearman correlation analysis was conducted to examine the relationship between the PPMI and IAT scores. The analysis denotes a strong positive correlation between the two measures r\u003csub\u003es\u003c/sub\u003e(408)\u0026thinsp;=\u0026thinsp;.65, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, suggesting that higher explicit prejudice scores were associated with higher implicit bias scores. This correlation is further illustrated by the scatter plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which demonstrates the linear relationship between explicit and implicit biases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Group comparisons\u003c/h2\u003e \u003cp\u003e \u003cb\u003eHistory of Mental Illness.\u003c/b\u003e The Mann-Whitney U test was employed to determine whether PPMI and IAT scores differed between participants with and without a history of mental illness. The results indicated that individuals with a history of mental illness exhibited significantly lower scores on both PPMI and IAT respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(z=-14.667;-14.582,p\u0026lt;0.001)\\)\u003c/span\u003e\u003c/span\u003e compared to those without such a history. The mean rank of PPMI score for people with prior history of mental illness was 91.29, as opposed to 252.41 for those without history. Also, the same for IAT was 91.77 compared to 252.01.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEducational Background.\u003c/b\u003e The Kruskal Wallis test showed there was a significant difference in PPMI and IAT scores across different types of qualifications attained, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:(4,\\:408=48.781),\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\left(4,\\:408=50.031\\right),p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e represents the boxplot of PPMI and IAT scores across different qualification backgrounds, illustrating the distribution and variability of scores and highlighting the differences between the groups as assessed by this test. The mean ranks of PPMI scores for people were 116.30 with medical degree, 216.16 for bachelor\u0026rsquo;s degree, 225.61 for high school, 221.79 for some college/university degree or vocational training and 230.14 for master\u0026rsquo;s degree. On the other hand, the mean ranks of IAT scores for people were 115.20 with medical degree, 217.89 for bachelor\u0026rsquo;s degree, 222.54 for high school, 221.23 for some college/university degree or vocational training and 232.68 for master\u0026rsquo;s degree. The post-hoc comparisons using Dunn\u0026rsquo;s method with a Bonferroni correction for multiple tests indicated that the PPMI and IAT scores of individuals with medical degree were significantly lower than that of people with other qualification degrees, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e in all pair of comparisons with respect to medical degree.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Phase 2\u003c/h2\u003e \u003cp\u003eThe analysis for this phase of the study commenced with Levene's Test to assess the homogeneity of variances across intervention groups for variables including Age, Gender, Highest Qualification, Occupation, History of Mental Illness, Experiences with Mental Health Services, and post-intervention scores of both PPMI and IAT. The results indicated that variances for all characteristics were homogeneous across the intervention groups, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026gt;0.05\\)\u003c/span\u003e\u003c/span\u003e for all sets (see Appendix C). Given the confirmed non-normal distribution of the scores, non-parametric tests were again employed for further analysis. The Independent Samples Kruskal Wallis test among pre-intervention scores across all experimental groups showed no significant difference in the distribution of both scores across all groups, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:\\left(3,\\:\\:408\\right)=1.961,\\:p=.580\\)\u003c/span\u003e\u003c/span\u003e for PPMI scores and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:\\left(3,\\:\\:408\\right)=1.555,p=.670\\)\u003c/span\u003e\u003c/span\u003e for IAT scores.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Intervention efficacy\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBetween-Group Comparisons.\u003c/b\u003e The Kruskal Wallis test assessed the difference scores across the four experimental groups: Direct Contact, Indirect Contact, Education Empowerment, and Control. The results indicated significant differences in prejudice levels among the groups \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:\\left(3,\\:\\:408\\right)=362.849,\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e for PPMI_Diff and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:\\left(3,\\:\\:408\\right)=341.135,\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e for IAT_Diff. The mean ranks of PPMI_Diff scores were 55.54 for Direct Contact, 156.45 for Indirect Contact, 251.48 for Education Empowerment Group and 354.53 for Control Group. Meanwhile, the same for IAT_Diff scores were 58.54 for Direct Contact, 172.22 for Indirect Contact, 229.75 for Education Empowerment Group and 357.50 for Control Group. The post-hoc comparisons using Dunn\u0026rsquo;s method with a Bonferroni correction for multiple tests showed that the difference PPMI and IAT scores of Control group was significantly higher than other groups (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePairwise Comparisons of difference PPMI Scores Across Intervention Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1-Sample 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Test Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdj. Sig.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Indirect Contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-100.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Education empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-298.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Contact-Education empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Contact-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-198.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation empowerment-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-103.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eEach row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAsymptotic significances (2-sided tests) are displayed. The significance level is .05.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003ea\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificance values have been adjusted by the Bonferroni correction for multiple tests.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePairwise Comparisons of difference IAT Scores Across Intervention Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample 1-Sample 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Test Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdj. Sig.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Indirect Contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-113.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Education empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect contact-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-298.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Contact-Education empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Contact-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-185.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation empowerment-Control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-127.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eEach row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAsymptotic significances (2-sided tests) are displayed. The significance level is .05.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003ea\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificance values have been adjusted by the Bonferroni correction for multiple tests.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eWithin-Group Comparisons.\u003c/b\u003e A Wilcoxon Signed Rank test comparing pre- and post-intervention scores within all groups indicated that all experimental groups showed significant differences in both PPMI and IAT scores. However, there was no significant difference for the Control group (Z = -1.102, p\u0026thinsp;=\u0026thinsp;.270 for PPMI; Z = -1.908, p\u0026thinsp;=\u0026thinsp;.065 for IAT), confirming that the observed changes. Specifically, the mean pre-intervention PPMI score for the Control group was 0.036 (SD\u0026thinsp;=\u0026thinsp;0.527), which remained barely changed post-intervention (mean\u0026thinsp;=\u0026thinsp;0.045, SD\u0026thinsp;=\u0026thinsp;0.526). Similarly, the mean pre-intervention IAT score 0.485 (SD\u0026thinsp;=\u0026thinsp;0.456), also remained unchanged post-intervention (mean\u0026thinsp;=\u0026thinsp;0.485, SD\u0026thinsp;=\u0026thinsp;0.456).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe paper aimed to elucidate the interaction between explicit and implicit prejudices in provoking discriminatory attitudes and behaviors towards people with mental health conditions. It also sought to experimentally analyze the effectiveness of various interventions in mitigating those prejudicial attitudes. The results of the study are discussed in the context of the hypotheses formulated, evaluates the significance of the findings, compares them with previous research, and explores their implications for the field of psychology.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Hypothesis 1\u003c/h2\u003e \u003cp\u003eIt proposed that a significant correlation between explicit and implicit biases against mental illness existed. The findings from the Spearman correlation analysis support this hypothesis, indicating a strong positive correlation between PPMI and IAT scores. This suggests that individuals who exhibit higher levels of explicit prejudice also tend to harbor stronger implicit biases and vice versa. These results are consistent with previous research that has found significant correlations between explicit and implicit measures of prejudice across various domains, including racial biases and attitudes toward different social groups (Gonz\u0026aacute;lez-Sanguino et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stier \u0026amp; Hinshaw, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, those reports contained lesser values of rho than this result. Probably, because of distortions in the response tendencies and social desirability in assessing explicit prejudices. Yet, the strong correlation observed in this study aligns with the Dual-Process Theory, which posits that implicit and explicit attitudes can influence behavior in different yet interconnected ways (Kahneman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This theory suggests that while explicit attitudes are consciously controlled and can be modified through direct interventions, implicit attitudes operate automatically and may require different strategies for change.\u003c/p\u003e \u003cp\u003eThe strong correlation between scores reveals that mental illness stigma is deeply ingrained, affecting both conscious and unconscious attitudes. This finding underscores the critical need to address both explicit and implicit biases, as focusing on only one may be insufficient to ameliorate them properly. It also highlights the importance of developing integrated interventions that can simultaneously combat both forms of prejudice to effectively reduce stigma. By understanding this dual pathway through which stigma operates, mental health professionals and policymakers can develop more effective strategies to combat stigma and promote more accepting and supportive attitudes toward individuals with mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Hypothesis 2\u003c/h2\u003e \u003cp\u003eIt posited that individuals with a history of mental illness would exhibit significantly lower scores on both PPMI and IAT scores compared to those without such a history. The significantly lower scores of individuals with a history of mental illness illuminate the profound impact of personal experience on reducing stigma. This outcome suggests that those who have personally navigated the challenges of mental illness develop more empathetic and less prejudicial attitudes, likely stemming from a deeper understanding and awareness of the complexities associated with mental health conditions. Such findings are consistent with existing research, which often underscores that firsthand experience can significantly diminish negative biases and foster more supportive perspectives. Moreover, these results underscore critical implications for stigma reduction strategies (Maunder \u0026amp; White, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Adu et. al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They highlight the potential efficacy of interventions that facilitate the sharing of personal experiences with mental illness. Educational programs and anti-stigma campaigns that incorporate narratives or testimonials from individuals who have lived through mental health challenges can effectively diminish both forms of biases. This approach is well-aligned with the Contact Theory, which asserts that interpersonal interactions with stigmatized groups can significantly reduce prejudices. In essence, this observed lower stigma scores emphasize the transformative power of personal experience in combating stigma (Allport, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1954\u003c/span\u003e). By integrating personal stories and lived experiences into intervention strategies, mental health professionals can create more compelling and relatable content that resonates with a wider audience. This method can play a crucial role in fostering greater acceptance and understanding of mental health issues, thereby contributing to a more inclusive and supportive societal attitude toward mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Hypothesis 3\u003c/h2\u003e \u003cp\u003eIt stated that participants with a medical education background would have significantly lower scores on both tests than those from other educational backgrounds. The widely held notion that specialized knowledge and training can mitigate stigmatizing attitudes came true through this result which indeed showed significantly lower scores of participants from medical education background. This result suggests that medical education, which typically includes comprehensive information about mental health disorders, their causes, and treatment options, alongside the clinical rotations within psychiatry during their training can effectively reduce both explicit and implicit biases against mental illness. Such comprehensive theoretical and practical exposure likely fosters a more nuanced and empathetic understanding of mental health issues, thereby diminishing prejudicial attitudes. These results resonate with previous studies that have demonstrated the efficacy of medical education in reducing mental health stigma (De Witt et. al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Papish et. al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Theoretical perspectives such as the Dual-Process Theory provide further insight into these findings. Medical education, with its detailed and systematic approach to mental health, likely influences both pathways of Dual-Process theory, \u003cem\u003eSystem 1\u003c/em\u003e and \u003cem\u003eSystem 2\u003c/em\u003e, promoting more informed and less biased attitudes. Moreover, the significant differences observed between medical and non-medical participants underscore the importance of targeted educational interventions. For instance, integrating mental health education into broader educational curricula could be a powerful tool in reducing stigma across diverse populations. By providing individuals with accurate information and fostering a deeper understanding of mental health issues, such educational initiatives can substantially contribute to reducing prejudicial attitudes and promoting a more supportive and inclusive attitude towards individuals with mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Hypothesis 4\u003c/h2\u003e \u003cp\u003eThe implications of previously examined hypotheses led to designing an experiment that first proposed possible interventions and later analyzed their relative effectiveness towards combating prejudices. The current hypothesis focused on evaluating the efficacy of a direct contact intervention program on reducing prejudice, compared to control and other alternative intervention groups. The results provided robust evidence supporting the hypothesis that individuals undergoing direct contact with individual who had navigated all the challenges including onset, prognosis and stigma would demonstrate a significantly greater reduction in prejudice scores.\u003c/p\u003e \u003cp\u003eThe thorough application of various statistical tests provided a solid foundation for hypothesis testing and ensured the robustness of the study\u0026rsquo;s conclusions. The homogeneity as evidenced by Levene\u0026rsquo;s test with all variables, insignificant different score of control group as shown via Related Samples Wilcoxon-Signed Rank test between pre-and post-intervention test scores and significant difference in pre-intervention test scores across all groups as proven by Independent-Samples Kruskal Wallis test collectively concluded that the significance of Independent Samples Kruskal Wallis test of post-intervention test scores across all groups was indeed the result of intervention programs ruling out all possible variables that would have impacted the study otherwise. This along with Post hoc Dunn's test elucidated that the direct contact intervention group experienced a significantly greatest reduction in prejudice scores compared to both the control and alternative intervention groups. Specifically, the largest negative z-values observed in the comparison between the direct contact and control groups (-298.99 for PPMI and \u0026minus;\u0026thinsp;298.96 for IAT) highlight the substantial effectiveness of direct contact in reducing prejudice. Similarly, the indirect contact group also demonstrated a significant reduction in prejudice scores, though less pronounced than the direct contact group (z = -198.078 for PPMI and z = -185.28 for IAT) compared to the control group. The education empowerment group, while showing the smallest impact, still achieved a notable reduction in prejudice scores relative to the control group (z = -103.054 for PPMI and z = -127.76 for IAT). These findings suggest a gradient of effectiveness, with the direct contact intervention being the most effective, followed by the indirect contact group, and then the education empowerment group.\u003c/p\u003e \u003cp\u003eThe Direct Contact group's effectiveness can be attributed to the unique advantages it offers in humanizing individuals with mental illness. This finding aligns with Contact theory, which posits that direct interaction with members of stigmatized groups can reduce prejudice by fostering empathy and breaking down stereotypes. The result is also consistent with other similar studies; for example, Atienza-Carbonell et. al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted an experiment with medical students, where interactions with patients who also served as educators led to a significant reduction in stigma. The personal stories and direct experiences shared in our group provided participants with a deeper, more relatable understanding of mental illness, which is often lacking in more impersonal forms of education. In comparison, the indirect contact group, which engaged with the film \u003cem\u003eTaare Zameen Par\u003c/em\u003e, also showed significant reductions in stigma, The film, through its depiction of a young boy's struggles with dyslexia, offered a powerful narrative that elicited empathy and reflection. This also aligns with previous research suggesting that media portrayals can effectively reduce stigma by providing relatable and emotional stories. In contrast, Poulgrain et. al (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in their recent study found increment in PPMI scores of participants who watched a film \u003cem\u003eJoker\u003c/em\u003e, movie depicting the dangerousness of a mentally ill person. Meanwhile, the education empowerment group, which focused on providing factual information and raising awareness about mental health issues, showed some reduction in stigma but to the least extent among the groups. This suggests that while knowledge and awareness are essential components of stigma reduction, they may not be as effective in isolation. Educational interventions might lack the emotional engagement necessary to deeply challenge and change prejudicial attitudes, highlighting the need for integrated approaches that combine information with personal narratives or interactions. Besides, the superior efficacy of direct contact interventions suggests prioritizing experiential learning in anti-stigma efforts. Given the relative effectiveness observed, combining contact in addition to education empowerment programs could potentially enhance the overall impact on prejudice reduction. This combined approach can leverage the strengths of both contact-based interaction and educational empowerment, offering a more comprehensive strategy to address and reduce prejudice. These insights collectively inform policymakers, educators, and mental health professionals about effective strategies to foster more inclusive and supportive attitudes towards individuals with mental illness, thereby contributing to more empathetic and informed public health initiatives.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Limitations and future recommendations","content":"\u003cp\u003eDespite the promising results, this study has few limitations that should be acknowledged. One significant limitation arises from the use of convenience and snowball sampling, which can introduce sample bias, potentially hindering the generalizability of findings. Additionally, the reliance on self-report measures for assessing prejudice may be subject to social desirability bias. While the study focused on short-term impacts of the interventions focusing on specific temporal snapshot, long-term follow-up studies are needed to assess the sustainability of the observed changes in attitudes. Also, the experimental phase designed to investigate cognitive mechanisms, is subject to constraints stemming from its artificial, controlled nature, potentially affecting the ecological validity of results.\u003c/p\u003e \u003cp\u003eBuilding on the current study, examining the specific components of the direct contact intervention that contribute most significantly to prejudice reduction could inform the development of more targeted and effective programs. Further research should also explore the differential impact of these interventions across diverse demographic groups. Understanding how factors such as age, gender, socioeconomic status, and cultural background influence the effectiveness of anti-stigma interventions can help in tailoring programs to better meet the needs of specific populations. Additionally, future research could investigate the synergistic effects of combining direct contact with educational empowerment to enhance the efficacy of prejudice reduction efforts.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis research provides valuable insights into the mechanisms driving mental illness stigma and contributes significantly to the understanding of how different interventions can reduce prejudice. The findings advocate for the implementation of contact-based programs and suggest that combining them with educational strategies could enhance their effectiveness. The implications of these findings extend to policymakers, educators, and mental health professionals, offering a roadmap for developing more inclusive and supportive environments for individuals with mental illness. By addressing both explicit and implicit biases through comprehensive and targeted interventions, we can foster a society that embraces mental health with empathy and understanding.\u003c/p\u003e"},{"header":"Glossary","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Attitude: A relatively enduring and general evaluation of an object, person, group, issue, or concept on a dimension ranging from negative to positive (APA, 2018). For example, Ali has positive attitudes towards environmental conservation; he always recycles, uses public transportation, and advocates for green policies.\u003c/p\u003e\n\u003cp\u003eBias: An inclination or predisposition for or against something (APA, 2018). For example, the hiring manager\u0026apos;s preference [bias] toward applicants from prestigious universities led him to overlook qualified candidates from lesser-known schools.\u003c/p\u003e\n\u003cp\u003ePrejudice: A negative evaluation characterized by cognitive and affective responses that subsequently trigger discriminatory behavior (G\u0026ouml;rzig \u0026amp; Ryan, 2022). For instance, despite never having met anyone from the country, Sarah held a prejudice [negative evaluation] against people from that region, believing they were all unfriendly and untrustworthy.\u003c/p\u003e\n\u003cp\u003eStereotype: A cognitive structure fixed and over-generalized that divide people into groups or categories (Corrigan, 2018). For instance, the widely held image [stereotype] that all elderly people are bad with technology is unfair and inaccurate, as many seniors are quite proficient with smartphones and computers.\u003c/p\u003e\n\u003cp\u003eStigma: The social devaluation of individuals and groups based on attributes they possess that mark them as discredited along specific axes of social desirability (Goffman, 2009). For example, the stigma around mental illness prevents people from seeking help they need due to the fear of judgement and discrimination.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The author extends deepest gratitude to Bela Khan for her unwavering guidance and mentorship throughout every facet of this academic journey. Her expertise and support have been crucial in the author\u0026apos;s development as a scholar.\u003c/p\u003e\n\u003cp\u003eThe author is also profoundly grateful to Sunaina Awasthi, who candidly shared her experiences to one of the experimental groups. Her valuable insights on combating mental illness stigma and the challenges in treatment have not only enriched this study but also provided a powerful testament to resilience and hope for many.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: Credit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe author solely contributed to all aspects of the research, including conceptualization, methodology, formal analysis, investigation, data curation, writing\u0026mdash;original draft preparation, and editing, visualization, supervision, project administration, and so on.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration and Human Participation Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ensuring ethical integrity in research is paramount, especially when dealing with sensitive topics like mental health. The current study adhered strictly to ethical guidelines approved by the Institutional Review Board at International Open University, to protect participants\u0026apos; rights, dignity, and well-being. Comprehensive informed consent was obtained, ensuring participants were aware of the study\u0026apos;s purpose, procedures, potential risks, benefits, and their right to withdraw at any time without penalty. Confidentiality was rigorously maintained by anonymizing any identifying information and securely storing data. Participants were thoroughly debriefed at the study\u0026apos;s commencement, with access to mental health support resources provided throughout the study. The study protocol was duly signed and approved by the concerned body, ensuring compliance with ethical standards for human subject research. Efforts were made to minimize harm, including providing support resources to participants in the direct contact group. Participation was entirely voluntary, with no coercion involved, particularly in the convenience and snowball sampling methods used. Cultural sensitivity was emphasized, with research tools and interventions adapted to the Nepalese context to ensure relevance and respect for participants\u0026apos; cultural backgrounds and beliefs. The study also included regular check-ins with participants to monitor their well-being and offered additional support if needed. By adhering to these ethical principles, the study safeguarded participant well-being and upheld research integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe author used ChatGPT, a generative AI model, to assist in paraphrasing certain sections of this manuscript to enhance its readability and linguistic quality. However, the author has meticulously reviewed, edited, and refined the content as necessary and assumes full responsibility for the accuracy and integrity of the final published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdu, J., Oudshoorn, A., Anderson, K., Marshall, C. A., \u0026amp; Stuart, H. (2021). 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The Subtle Side of Stigma: Understanding and Reducing Mental Illness Stigma from a Contemporary Prejudice Perspective. \u003cem\u003eJournal of Social Issues, 75\u003c/em\u003e(3), 943\u0026ndash;971. https://doi.org/10.1111/josi.12343\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Transcranial Magnetic Stimulation\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Bollywood movie directed by Aamir Khan in 2007\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Measuring beliefs in the dangerousness of individuals with mental illness and the desire for social distance from them.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Assessing the belief that the behavior of individuals with mental illness is unpredictable.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Capturing beliefs in the need to coercively control individuals with mental illness.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Reflecting beliefs in the inferiority of individuals with mental illness and a lack of sympathy for them.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mental illness stigma, prejudice, PPMI, IAT, interventions, effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-5014975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5014975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMental illness stigma is a pervasive issue that transcends cultural and national boundaries, presenting substantial impediments to successful treatment, reducing key life opportunities, and exacerbating poor outcomes beyond the direct effects of the illness itself. This research primarily investigated the prejudices associated with mental illness, focusing on their combined manifestation through explicit and implicit biases. It aimed to demonstrate how these prejudices contribute to discrimination, thereby aggravating the primary symptoms of mental disorders. Additionally, the study explored the most efficacious intervention strategies aimed at mitigating these biases. The assumption was that participants with priorly direct contact with mentally ill individual would demonstrate significant reduction in their prejudice level. The sample (\u003cem\u003en\u0026thinsp;=\u0026thinsp;408\u003c/em\u003e) consisted of Nepalese individuals from diverse demographic backgrounds, aged between 18 and 60. They initially completed the Prejudice towards People with Mental Illness (PPMI) scale measuring explicit prejudice, and the mental illness Implicit Association Test (IAT) assessing implicit prejudice. Subsequently, they were randomly assigned to one of four distinct groups: direct contact, indirect contact, education empowerment, and a control group; each incorporating interventions except control group. After two weeks of corresponding exposures, both tests were readministered to evaluate changes in scores. The differences in both scores were calculated to determine the impact of interventions. A Kruskal-Wallis test for changes across groups indicated there was a significant difference, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:2\\:\\left(3,\\:\\:408\\right)=(362.849;\\:341.135),\\:p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e for PPMI and IAT scores respectively. Post-hoc comparisons using Dunn\u0026rsquo;s method with a Bonferroni correction for multiple tests indicated that the mean changes in PPMI and IAT for the group engaging in direct contact with mentally ill individual were significantly lower than other groups. This implies that stigma reduction programs should incorporate direct interaction with individuals who have experienced mental illness. Given the uncertain long-term effectiveness of these interventions, it is essential to conduct extended research to evaluate their sustained impact.\u003c/p\u003e","manuscriptTitle":"Understanding and Addressing Prejudices Faced by Mentally Ill Individuals: A Multidimensional Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 10:14:56","doi":"10.21203/rs.3.rs-5014975/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8062c1be-4b25-4718-93ce-dbd58e87e85c","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 10:14:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5014975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5014975","identity":"rs-5014975","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}