Latent Profiles and Correlates of Mental Health Stigma in India

Abstract

Background:

Stigma toward mental health problems is now being understood as a context-dependent, multidimensional concept. Nonetheless, empirical research continues to study stigma as a monolithic aspect, obscuring meaningful within-group variations. Such inquiries often obscure the people-centered patterns of stigma within the population. Recognizing such patterns aids in better understanding how stigma differentially influences help-seeking and resilience in the context of mental health.

Aims:

Accordingly, the present study seeks to identify latent profiles for stigmatizing attitudes and their correlates among Indian adults. The study also explores how these profiles potentially differ in resilience and help-seeking attitudes.

Methods:

The data were collected via a cross-sectional survey with 564 Indian adults. Standardized self-report measures like the Attitudes Toward Mental Health Problems Scale, the Connor-Davidson Resilience Scale – 10-item version, and the Inventory of Attitudes Toward Seeking Mental Health Services were used.

Results:

Latent Profile Analysis revealed a four-profile model that differed significantly in resilience and help-seeking attitudes. Further, a multinomial logistic regression revealed attitudes toward seeking mental health services, accessibility, reliance on religion, and mental health awareness as correlates of profile membership.

Conclusion:

The findings indicate the presence of multiple patterns of stigmatizing attitudes among Indian adults. The results of this study could inform practice, policy, and context-sensitive interventions to reduce stigma and promote the use of mental health services.

Keywords

attitudes help-seeking mental health problem resilience stigma

Introduction

Stigma remains a major barrier to effective management of mental health problems. Besides hindering help-seeking behaviors, it interferes with the development of resilience and adaptive coping. Walsh and Foster (2021) note that stigma is understood as a multidimensional concept characterized by group differences, loss of status, social distance, and discrimination. Accordingly, stigma and its impact emerge at multiple levels, connecting individual experiences with sociocultural processes (Holder et al., 2019). More recently, scholars like Andersen et al. (2022) argue that stigma is targeted at groups while individuals may experience its impact.

In agreement, scholars, including Goffman (2009) and Dolezal (2022), identified shame as the phenomenological component of stigma. Hutchinson (2025) added that stigma is part of the shame experience, rather than being an external cause. A notable work in this area is by Gilbert et al. (2007), who identified external, internal, and reflected shame in the Asian context. According to these authors, external shame is based on an individual’s perception of social stigma. Similarly, internal shame is comparable to self-stigma. Corrigan and Rao (2012) described self-stigma as the internalization of public attitudes. Gilbert et al. (2007) argued that internal shame stems from self-appraisal based on these internalized public attitudes. The authors also highlighted reflected shame as a culturally unique expression of stigma, where a person’s condition can bring shame to the group they belong to. In their Attitudes Toward Mental Health Problems (ATMHP) scale, Gilbert et al. (2007) further differentiated reflected shame. Family-reflected shame refers to the shame brought to one’s family due to their mental health problem. Self-reflected shame arises due to a close family member’s mental illness. These patterns of shame may be understood in relation to courtesy stigma or stigma by association (Goffman, 2009).

Thus, the different kinds of shame can form diverse constellations within individuals corresponding to various patterns of stigma in the population. These patterns are shaped by temporal and cultural factors (e.g., Lu et al., 2025). Therefore, it is important to adopt a bottom-up, context-specific, individual-centric approach to understanding stigma. The present study situates such an inquiry in the Indian context.

Stigma and Shame in India

Mental health problems remain deeply stigmatized in the Indian context. Scholars such as Gaiha et al. (2020) associate this stigma with poor mental health literacy. The authors noted several misunderstandings regarding the etiology of mental health problems, ranging from religious explanations to moral failures. In such contexts, mental health problems may be understood as deviations from societal norms, leading to blaming and shaming (Sum et al., 2024). Under these circumstances, culturally sanctioned practices such as traditional healing methods appear to buffer against shame (Biswal et al., 2017). For this reason, many Indians resort to these methods before consulting clinical mental health services (Khemani et al., 2020). However, the delayed help-seeking often worsens the condition. In such contexts, individuals may attribute their conditions to fatalistic beliefs such as Karma. Vaishnav et al. (2023) observe that such explanations hold individuals responsible for their condition while also depriving them of agency through fatalistic explanations. In this way, traditional practices paradoxically reinforce shame and stigma toward mental health problems in India.

In India, shame related to mental health problems is experienced by the individual, family, and the community (Gilbert et al., 2007). Van Herpen (2023) argues that in Eastern societies such as India, individual shame is often a collective experience. For instance, concerns about family reputation and matrimonial prospects have been commonly reported expressions of shame in India (e.g., Raghavan et al., 2023). This suggests an overwhelming influence of external shame, even in the absence of internal shame in these contexts. Such unique constellations may not be captured in the current categorizations of shame. This has implications for clinical practice, health policy, and interventions to reduce stigma and promote help-seeking.

Stigma, Help-Seeking, and Resilience

The National Mental Health Survey 2015 to 2016 noted that 84.5% of Indians with mental health problems are not getting the required treatment, with stigma being a key factor (Gautham et al., 2020). Moitra et al. (2023) observe that by 2020, most countries failed to reach the targets for mental health care set by the World Health Organization and the United Nations. These authors concur that stigma continues to be a significant barrier to help-seeking internationally despite concerted efforts to address it. Holder et al. (2019) add that delayed help-seeking often makes symptoms worse and leads to a poor prognosis. In such situations, individuals may adopt maladaptive coping strategies like social withdrawal (Ahad et al., 2023). The resulting depletion of the individual’s self-esteem and efficacy can undermine resilience (King et al., 2024). Lower resilience, in turn, contributes to greater stigma and poses a threat to recovery and positive adaptation for people with mental health problems (Crowe et al., 2016).

Based on the above discussion, it is evident that a people-centered approach to identifying stigma through shame-based measures is imperative. Besides evaluating existing theory, such an approach helps to explore cultural variations. Additionally, the bottom-up approach aids in tailoring stigma reduction interventions more efficiently. Identifying people-centered patterns of stigma can also help understand potential differences in help-seeking and resilience. This helps contextualize mental health research and practice. Considering these rationales, this study aims:

To explore latent profiles for stigma among Indian adults.

To examine if the latent profiles differ across ASMHS and resilience.

To identify the correlates of profile membership.

Methods

Design and Ethics

This study used a quantitative, cross-sectional design using an online survey. Ethics approval for the study was obtained from the Institutional Ethics Committee (Protocol number hidden for peer review). Informed consent was obtained digitally via a Google Form containing details about the study, ethical principles followed, and participants’ rights.

Sample and Participants

This study targeted a broad population of Indian adults aged 18 to 59 years. It was not feasible to use probabilistic sampling techniques for such a vast and varied population, given the lack of a sampling frame. Therefore, a combination of convenience, purposive, and snowball sampling was used to recruit participants. Participants were only recruited online by sharing the Google Form via WhatsApp, Reddit, and LinkedIn. Consequently, response rates could not be computed, and the risk of self-selection bias exists in the sample. However, scholars like Sanchez et al. (2020) argue that online recruitment is comparable to traditional methods of contacting participants. Moreover, certain steps were taken to maximize the sample representation. For example, participants were purposively approached based on their occupation in the mental health field and geographical location.

GPower 3.1 software, developed by Faul et al. (2009), was used to conduct an a priori power analysis. Since the software lacked a direct estimation technique for multinomial logistic regression, power analysis was conducted for a proxy linear regression model. For an assumed medium effect size (f² = 0.15), α = .05, and power (1 − β) = 0.80, a sample of 189 participants was recommended. However, considering the potential use of other analytical techniques and based on the sample sizes reported in similar studies, data were collected from 581 individuals. Of these, 564 responses were found usable in the analysis.

Participants aged 18 to 59 years who were currently residing in India and could read and write English were included in this study. Responses that did not meet these criteria were eliminated at the data screening stage. The final sample of 564 Indian adults comprised 304 females and 260 males. The mean age was 28.91 (SD = 9.82). Most participants had postgraduate education (N = 277) and were single (N = 297).

Variables and Measures

Socio-Demographic Variables

Non-standardized single items were used to collect information regarding age, gender, highest educational qualification, and relationship status. These items were either open-ended or multiple-choice. Additionally, non-standardized single items with a Likert scale were used to measure financial satisfaction, social satisfaction, and reliance on religion for coping.

Mental Health-Related Variables

Non-standardized single items with a Likert or binary response option were used to collect information on mental health-related variables. These variables include accessibility to mental health services, education in mental health-related fields, exposure to people with mental health problems, perceived mental health awareness, and previous help-seeking experiences.

ATMHP

The 35-item ATMHP scale by Gilbert et al. (2007) was used to measure ATMHP. The scale measures stigmatizing attitudes and shame about mental health problems along five factors: attitudes, external shame, internal shame, family-reflected shame, and self-reflected shame. The responses are collected using a Likert scale ranging from 0, representing “Do not agree at all,” to 3, representing “Completely agree.” The possible scores range from zero to 105, with higher scores corresponding to greater shame. Previous studies, such as Cabral Mater et al. (2016) and Poudel and Timalsina (2025), reported excellent psychometric properties for the scale, demonstrating acceptable reliability and supporting the factor structure across Western and Asian contexts. In this sample, the Cronbach’s alpha was found to be .95.

Resilience

Resilience was measured using the Connor-Davidson Resilience Scale 10-item version (CD-RISC-10) by Campbell-Sills and Stein (2007). The unidimensional scale uses a Likert scale ranging from 0, “not true at all,” to 4, “true nearly all the time.” The possible range of scores is from zero to 40, with higher scores indicating higher resilience. CD-RISC-10 has consistently reported excellent reliability and validity across different populations (Sharif-Nia et al., 2024). In this sample, the Cronbach’s alpha was observed to be .90.

ASMHS

The 24-item Inventory of Attitudes Toward Seeking Mental Health Services by Mackenzie et al. (2004) was used to measure ASMHS. This scale measures ASMHS by using three factors: psychological openness, help-seeking propensity, and indifference to stigma. It uses a five-point Likert scale with response options ranging from 0, representing “disagree,” to 4, representing “agree.” The possible range of scores spans from zero to 96, with higher scores corresponding to more favorable ASMHS. The extant literature reports good psychometric properties of the scale in the Indian context (e.g., Amudhan et al., 2021). The Cronbach’s alpha for this sample is .84.

Analysis

Manually identified incomplete responses and outliers detected using the Mahalanobis distance were removed from the original 584 responses. The remaining 564 responses were used for further analysis. Latent Profile Analysis (LPA) was conducted separately for ATMHP, resilience, and ASMHS using the MPlus 8 demo version. Considering the exploratory use of LPA in this study, the number of profiles was not determined a priori. Instead, LPA was conducted using a trial-and-error method. The trials were terminated once the entropy values and class proportions started declining in two subsequent trials. Scholars like Spurk et al. (2020) recommend using multiple fit indices in determining model fit. Accordingly, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (SSABIC), entropy values, and interpretability of profiles were used to identify the number of profiles in this study. The reliability of the latent profiles was evaluated by replicating the best log-likelihood by increasing the starts up to 4,000 initial and 1,000 final stage starts. Spurk et al. (2020) explain that since LPA uses maximum likelihood estimation, increasing the starts provides Mplus with more random starting points to find the best possible model. The codes used for conducting the LPA are included in the supplementary material.

Differences across latent profiles in key psychological variables, such as resilience and ASMHS, were examined using a MANOVA. Further, a multinomial logistic regression was conducted to identify variables associated with the membership in these profiles.

Results

Table 1 depicts the LPA for ATMHP. Models with four, six, and eight profiles have entropy values above the threshold of .80, recommended by Weller et al. (2020). Besides, all three models have profile proportions equal to or greater than 5%. Hipp and Bauer (2006) caution against profile proportions below 5%, which indicate instability in the model. However, the Lo-Mendel-Rubin test for the eight-profile model was non-significant, suggesting poor fit. Therefore, four-and-six profile models were tested for replicating the best loglikelihood. Only the four-profile model replicated the best loglikelihood, indicating greater model stability.

Table 1.

Latent Profiles for ATMHP.

Criteria/classes	2	3	4	5	6	7	8
Log-likelihood	−8677.357	−8594.617	−8529.080	−8479.389	−8441.505	−8.403.144	−8336.57
AIC	17386.714	17233.234	17114.160	17026.777	16963.009	16898.288	16777.154
BIC	17456.075	17328.605	17235.541	17174.169	17136.412	17097.700	17002.577
SSABIC	17405.283	17258.766	17146.655	17066.235	17009.431	16951.673	16837.502
Entropy	0.799	0.787	0.825	0.798	0.914	0.865	0.909
Class proportions	0.55, 0.44	025, 0.43, 0.31	0.42, 0.21, 0.19, 0.17	0.23, 0.29, 0.14, 0.15, 0.16	0.32, 0.21, 0.08, 0.18, 0.09, 0.08	0.22, 0.17, 0.06, 0.19, 0.12, 0.07, 0.144	0.05, 0.30, 0.13, 0.05, 0.07, 0.17, 0.10, 0.09
Lo-Mendell-Rubin	p = .000	p = .000	p = .06	p = .0002	p = .006	p = 1.000	p = .057
Bootstrap-likelihood	p = .000	p = .000	p = .000	p = .000	p = .000	p = 1.000	p = .000

Table 2 displays the mean and standard error for each of the five factors of ATMHP across the four profiles. Among the four profiles, Profile 1 has the lowest scores on all five factors, and therefore, it is named the Low-Stigma group. Profile 2 shows an interesting pattern with high scores for externally oriented factors like attitudes and external shame and low scores for internal factors like internal and reflected shame. Thus, Profile 2 was named External Stigma. Profile 3 indicated moderate scores across all five factors and was named Moderate Stigma. Participants in Profile 4 scored highest on all five factors. Therefore, Profile 4 was named High Stigma. Figure 1 visually depicts the differences among the four profiles based on their scores on the five factors of ATMHP.

Table 2.

Means and Standard Errors for the Four Profile Model.

Latent profiles and ATMHP subscales	M	SE
Low stigma (latent profile 1)
Attitudes	8.69	0.38
External shame	7.02	0.55
Internal shame	3.17	0.38
Family-reflected shame	4.38	0.48
Self-reflected shame	1.37	0.36
External stigma (latent profile 2)
Attitudes	17.40	0.60
External shame	21.87	0.90
Internal shame	6.57	0.55
Family-reflected shame	11.74	0.66
Self-reflected shame	1.96	0.29
Moderate stigma (latent profile 3)
Attitudes	10.45	0.73
External shame	12.22	0.97
Internal shame	6.98	0.76
Family-reflected shame	9.75	0.95
Self-reflected shame	8.29	1.13
High stigma (latent profile 4)
Attitudes	18.16	0.87
External shame	22.02	1.18
Internal shame	9.08	0.58
Family-reflected shame	15.00	0.71
Self-reflected shame	11.48	0.41

MANOVA

Each participant was assigned their most likely profile based on the co-probabilities. A one-way MANOVA was then conducted to understand if resilience and ASMHS varied significantly across the four latent profiles of ATMHP.

Among the major assumptions, the sample demonstrated independence of observations and had no multicollinearity. However, the Box’s M test was significant (p = .002), suggesting heterogeneity of covariance matrices. Similarly, Levene’s test for homogeneity of variances was not met for resilience. Besides, the sample did not follow a multivariate normal distribution. Since several major assumptions were breached, both Pillai’s trace and Wilk’s Ʌ are reported here. The details of the assumption checks are included in the supplementary material.

The results indicate a significant difference in resilience and ASMHS across the four latent profiles of ATMHP, Pillai’s Trace = .23, Wilk’s Ʌ = .77, F (6, 1120) = 25.31, p < .001, partial η² = .11. The test of between-subjects effects shows significant differences for both resilience (F (3, 560) = 14.83, p < .001, partial η² = .07) and ASMHS (F (3, 560) = 38.74, p < .001, partial η² = .17). Given the significant Levene’s test for resilience, Welch’s test was also conducted for robustness. The results confirmed statistically significant differences in resilience across the four profiles, Welch’s F (3, 246.70) = 14.83, p < .001.

The post hoc tests with Bonferroni corrections showed that the Low Stigma group had higher resilience than the External Stigma (MD = 5.86, p < .001) and the High Stigma (MD = 3.00, p = .01) groups. Additionally, the Moderate Stigma group showed higher resilience than the External Stigma group (MD = 4.50, p < .001). Similarly, the Low Stigma group reported more favorable ASMHS than the External Stigma (MD = 4.66, p = .01), Moderate Stigma (MD = 10.34, p < .001), and High Stigma (MD = 15.94, p < .001) groups. Also, participants in the External Stigma group held more favorable ASMHS than those in the Moderate Stigma group (MD = 5.68, p = .007).

Multinomial Logistic Regression

A multinomial logistic regression was conducted to identify variables that significantly correlated with membership in the latent profiles of ATMHP. The High Stigma group (profile 4) was considered the reference category. This enabled comparisons to identify factors contributing to lower stigma. Hypothesized correlates included sociodemographic variables, mental health-related variables, resilience, and ASMHS.

The overall model was statistically significant, χ² (54) = 232.825, p < .001, indicating that the final model significantly improved upon the baseline. The model has moderate explanatory power with Cox and Snell R² = .338, Nagelkerke R² = .365, and McFadden R² = .157. The log-likelihood tests reported in Table S2 in the supplementary material indicate that reliance on religion and social satisfaction were the only significant sociodemographic correlates of the model. Both psychological variables, resilience and ASMHS, significantly improved the model fit. None of the mental health-related variables emerged as significant correlates of the model in the log-likelihood tests.

However, parameter estimates shown in Table 3 display two mental health-related variables with local effects on specific profiles despite not showing an overall effect on the model. Among these, people with accessibility to mental health care had lower odds of being in the low stigma (Profile 1; B = −.68, p = .044, Exp (B) = .505) and moderate stigma (Profile 3; B = −.81, p = .024, Exp (B) = .44) groups than the high stigma group. People with greater mental health awareness had higher odds of membership in the external stigma group (Profile 2; B = .376, p = .030, Exp (B) = 1.456) than the high stigma group. Among the sociodemographic variables, reliance on religion contributed to lower odds of membership in the low stigma group (B = −.29, p = .006, Exp (B) = .74) than the high stigma group. ASMHS emerged as the most consistent correlate of profile membership. More favorable ASMHS were associated with higher odds of belonging to profiles with lower stigma. Interestingly, social satisfaction and resilience were not associated with membership in specific profiles despite contributing to the overall model fit. Table 4 summarizes the differences between the four latent profiles.

Table 3.

Parameter Estimates for Significant Correlates.

Profiles	B	SE	Wald	df	Sig.	Exp (B)	95% confidence interval for exp (B)
Profiles	B	SE	Wald	df	Sig.	Exp (B)	Lower bound	Upper bound
1
Intercept	−7.564	1.503	25.339	1	0.000
Reliance on religion	−0.296	0.107	7.624	1	0.006	0.743	0.602	0.918
ASMHS	0.094	0.012	56.877	1	0.000	1.098	1.072	1.125
Accessibility	−0.684	0.339	4.073	1	0.044	0.505	0.260	0.980
2
Intercept	−5.348	1.838	8.464	1	0.004
Mental health awareness	0.376	0.173	4.723	1	0.030	1.456	1.038	2.044
ASMHS	0.065	0.013	24.904	1	0.000	1.067	1.040	1.095
3
Intercept	−3.494	1.548	5.092	1	0.024
ASMHS	0.034	0.013	6.960	1	0.008	1.034	1.009	1.060
Accessibility	−0.816	0.361	5.115	1	0.024	0.442	0.218	0.897

Note. Profile 4, High Stigma group is the reference category.

Table 4.

Summary of Latent Profiles.

Latent profiles	Descriptions	ASMHS	Resilience
Low stigma	Low scores on all subscales of ATMHP	Most favorable	Higher than External Stigma and High Stigma profiles
External stigma	High scores on attitudes and external shame; low scores on internal shame and reflected shame	More favorable than the Moderate Stigma profile and less favorable than the Low Stigma profile	Lower than Moderate Stigma and Low Stigma profiles
Moderate stigma	Relatively moderate scores on all subscales of ATMHP	Less favorable than the External Stigma and the Low Stigma profile	Higher than the External Stigma profile
High stigma	High scores on all subscales of ATMHP	Least favorable	Lower than the Low Stigma profile

Note. The subscales of ATMHP include attitudes, external shame, internal shame, family-reflected shame, and self-reflected shame.

Discussion

The present study identified four latent profiles for ATMHP in a sample of Indian adults and examined how they differed across resilience and ASMHS. Further, the findings revealed specific correlates of membership in these profiles.

The four latent profiles were named based on their relative scores on the five factors of ATMHP. These were Low Stigma, External Stigma, Moderate Stigma, and High Stigma. Scholars, like Loch et al. (2014), have found four profiles for stigma toward patients with schizophrenia in the Brazilian general population. These are “no stigma,” “labelers,” “discriminators,” and “unobstrusive stigma.” These profiles primarily distinguish participants by cognitive and behavioral aspects of stigma. However, the profiles in the present study foreground the affective component through shame. Besides, the External Stigma profile, with higher scores on external aspects such as attitudes and external shame than on internal shame and reflected shame, was particularly distinctive in this study. Such a pattern has not been reported in similar classifications of stigma into high, moderate, and low levels (e.g., Da Silva et al., 2021; Ho et al., 2018; Loch et al., 2013).

The prominence of external over internal shame in this profile conforms with the collectivistic expression of shame described by Gilbert et al. (2007). These authors explain how shame operated through the idea of izzat (honor) among South Asians. Disturbances to honor can result in external, internal, and reflected shame. Nevertheless, honor and its disruptions are often understood through external feedback. In this context, shame stemming from what others think about one’s mental health problems may become more important than internal or reflected shame. The cultural significance of external shame is reflected in stigma manifested through concerns over marital prospects (Raghavan et al., 2023).

Both resilience and ASMHS differed across the four profiles. Resilience was higher for the Low Stigma group than for the External Stigma and High Stigma groups. This finding aligns with previous studies by Crowe et al. (2016) and Post et al. (2021), who highlighted a negative association between stigma and resilience. Similarly, the Low Stigma group had the most favorable ASMHS among the four profiles. This is consistent with the extant literature, which underscores a negative association between ATMHP and ASMHS (e.g., Alluhaibi & Awadalla, 2022).

Notably, resilience was lower for the External Stigma group than the Moderate Stigma group, suggesting a pronounced influence of external stigma on resilience. Crowe et al. (2016) suggested that stigma hindering help-seeking might explain reduced resilience. However, the External Stigma group demonstrated more favorable ASMHS than the Moderate Stigma group. An alternative explanation is that external stigma might interfere with internal protective factors. This interpretation is supported by King et al. (2024), who noted how stigma directly undermines resilience by reducing self-esteem and self-efficacy.

The multinomial logistic regression model revealed that favorable ASMHS consistently associated with higher odds for membership in profiles with lesser stigma. Concurring with scholars like Shahwan et al. (2020), this finding maintains the association between lower stigma and favorable ASMHS. Among the sociodemographic variables, reliance on religion for coping correlated with lower odds for belonging to the Low Stigma group. Angelin et al. (2025) attributed this to religious etiologies that reinforce stigma, like the doctrine of Karma in India, which explains mental illnesses as caused by past sins. However, Zieger et al. (2016) found contrary evidence associating strong religious beliefs with lower perceived stigma in India. Nonetheless, the authors caution that their study investigated perceived and not actual stigma. They note that religious beliefs espousing an ideal society deter people from perceiving higher levels of stigma.

Among the mental health-related variables, accessible mental health care significantly correlated with lower odds for membership in the Low and Moderate Stigma groups. This suggests that mere accessibility does not warrant stigma reduction. In agreement with this finding, Weaver et al. (2023) note that stigmatizing attitudes pose a more substantial barrier to help-seeking despite increasing access in India. It is important to note that accessibility was not a significant correlate for the External Stigma group. However, mental health awareness was associated with higher odds of being in this group. This finding aligns with Kroska and Harkness’s (2021) experimental outcome that showed how targeting awareness only addresses personal stigma and not its external aspects.

The present study identifies person-centered latent profiles of ATMHP among the Indian population. The findings highlight how public stigma may operate independently of internalized stigma and call for revising stigma-reduction interventions. Although the study was exploratory, the differences in resilience and ASMHS across these profiles may inform policy and clinical practice. Furthermore, the correlates of these profiles suggest that targeting attitudinal change remains important despite structural interventions, such as improving accessibility. While prior work in low- and middle-income countries (e.g., Ahad et al., 2023) has emphasized structural changes to reduce stigma, the present study underscores the importance of complementing these initiatives with interventions that target attitudes. This suggests that attitudinal determinants of stigma may operate similarly across different contexts, highlighting the continued global relevance of attitudes and awareness-based interventions.

Limitations, Strengths, and Future Directions

The present study has a few limitations. The use of a cross-sectional design with non-probabilistic, web-based sampling produced a sample that leaned toward younger, educated, and digitally connected individuals. This limits the generalizability of the findings. Future studies may use different sampling techniques to improve representativeness. Additionally, the key variables were measured using standardized self-report measures, which may introduce social desirability bias, particularly on topics such as stigma. Studies using implicit measures and qualitative methods are encouraged to supplement these findings and explore them in greater detail. Finally, the study is situated within India’s complex sociocultural background, which uniquely shapes the experience of stigma. While this strengthens ecological validity and extends the psychological literature beyond WEIRD populations, it is important to cross-validate these findings in other cultural contexts.

Method-wise, the current study used an exploratory LPA. Like all LPAs, profile membership is probabilistic, and a certain degree of classification uncertainty is inherent. Although statistical and theoretical aspects guided the model selection, replication with independent samples is required to ensure the stability of the profiles. Additionally, the sample did not meet the normality and homogeneity assumptions required for the multinomial regression. While robust test statistics were used, the findings should still be interpreted with caution. Thus, the findings of the study must be supported with further research.

Despite these limitations, the present study makes several meaningful contributions. Primarily, it offers a novel, person-centered insight into stigmatizing ATMHP in a non-Western context. Additionally, this study examines factors that are associated with the constellation of stigma among individuals. These findings hold crucial theoretical and practical relevance, particularly for interventions to reduce stigma toward mental health problems.

Conclusion

The present study highlights how stigma is often layered through psychological and sociocultural factors. The presence of different levels and expressions of stigma calls for a more contextualized and tailored approach toward stigma reduction, especially in lower and middle-income countries. Additionally, improving ASMHS and building resilience may help reduce stigma toward mental health. Accordingly, these findings may inform mental health policy, interventions, and practice.

Supplemental Material

sj-docx-1-isp-10.1177_00207640261468250 – Supplemental material for Latent Profiles and Correlates of Mental Health Stigma in India

Supplemental material, sj-docx-1-isp-10.1177_00207640261468250 for Latent Profiles and Correlates of Mental Health Stigma in India by Ananda Krishnan and Amrita Deb in International Journal of Social Psychiatry

Footnotes

Acknowledgements

The authors would like to thank everyone who participated in this study. Additionally, we extend our gratitude to the Knowledge Resource Centre of IIT Hyderabad for providing access to relevant articles and resources. Finally, we are grateful to the University Grants Commission for funding this research.

ORCID iD

Ananda Krishnan

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: University Grants Commission, India

Declaration of Conflicting Interests

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

Supplemental Material

Supplemental material for this article is available online.

References

Ahad

A. A.

Sánchez-González

Junquera

(2023). Understanding and addressing mental health stigma across cultures for improving psychiatric care: A narrative review. Cureus, 15(5), Article e39549. https://doi.org/10.7759/cureus.39549

Alluhaibi

B. A.

Awadalla

A. W.

(2022). Attitudes and stigma toward seeking psychological help among Saudi Adults. BMC Psychology, 10(1), Article 216. https://doi.org/10.1186/s40359-022-00923-4

Amudhan

Jangam

Mani

Murugappan

N. P.

Sharma

Mahapatra

Burma

A. D.

Tiwari

H. K.

Ashok

Vaggar

Rao

G. N.

(2021). Project SUMS (scaling up of mental health in schools): Design and methods for a pragmatic, cluster randomized waitlist-controlled trial on integrated school mental health intervention for adolescents. BMC Public Health, 21(1), Article 2034. https://doi.org/10.1186/s12889-021-12086-9

Andersen

M. M.

Varga

Folker

A. P.

(2022). On the definition of stigma. Journal of Evaluation in Clinical Practice, 28(5), 847–853. https://doi.org/10.1111/jep.13684

Angelin

R, R.

Saravanan

James

F. C.

Samuel

. (2025). Experience of religiosity in caregiving for persons with serious mental illness: A qualitative study using interpretative phenomenological analysis from India. BMJ Open, 15(3), Article e090838. https://doi.org/10.1136/bmjopen-2024-090838

Biswal

Subudhi

Acharya

. (2017). Healers and healing practices of mental illness in India: The role of proposed eclectic healing model. Journal of Health Research and Reviews, 4(3), 89. https://doi.org/10.4103/jhrr.jhrr_64_17

Cabral Master

J. M.

Barreto Carvalho

C. M. D. O.

Motta

C. D.

Sousa

M. C.

Gilbert

. (2016). Attitudes towards mental health problems scale: Confirmatory factor analysis and validation in the Portuguese population. American Journal of Psychiatric Rehabilitation, 19(3), 206–222. https://doi.org/10.1080/15487768.2016.1197860

Campbell-Sills

Stein

M. B.

(2007). Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress, 20(6), 1019–1028. https://doi.org/10.1002/jts.20271

Corrigan

P. W.

Rao

(2012). On the self-stigma of mental illness: Stages, disclosure, and strategies for change. The Canadian Journal of Psychiatry, 57(8), 464–469. https://doi.org/10.1177/070674371205700804

10.

Crowe

Averett

Glass

J. S.

(2016). Mental illness stigma, psychological resilience, and help seeking: What are the relationships? Mental Health & Prevention, 4(2), 63–68. https://doi.org/10.1016/j.mhp.2015.12.001

11.

Da Silva

A. G.

Loch

A. A.

Leal

V. P.

Da Silva

P. R.

Rosa

M. M.

Bomfim

O. D. C.

Malloy-Diniz

L. F.

Schwarzbold

M. L.

Diaz

A. P.

Palha

A. P

. (2021). Stigma toward individuals with mental disorders among Brazilian psychiatrists: A latent class analysis. Brazilian Journal of Psychiatry, 43(3), 262–268. https://doi.org/10.1590/1516-4446-2020-0864

12.

Dolezal

(2022). Shame anxiety, stigma, and clinical encounters. Journal of Evaluation in Clinical Practice, 28(5), 854–860. https://doi.org/10.1111/jep.13744

13.

Faul

Erdfelder

Buchner

Lang

A. -G

. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

14.

Gaiha

S. M.

Taylor Salisbury

Koschorke

Raman

Petticrew

. (2020). Stigma associated with mental health problems among young people in India: A systematic review of magnitude, manifestations and recommendations. BMC Psychiatry, 20(1), 538. https://doi.org/10.1186/s12888-020-02937-x

15.

Gautham

M. S.

Gururaj

Varghese

Benegal

Rao

G. N.

Kokane

Chavan

B. S.

Dalal

P. K.

Ram

Pathak

Lenin Singh

R. K.

Singh

L. K.

Sharma

Saha

P. K.

Ramasubramanian

Mehta

R. Y.

Shibukumar

T. M.

Deuri

S. P.

Krishnatreya

, . . . Majhi

(2020). The National Mental Health Survey of India (2016): Prevalence, socio-demographic correlates and treatment gap of mental morbidity. International Journal of Social Psychiatry, 66(4), 361–372. https://doi.org/10.1177/0020764020907941

16.

Gilbert

Bhundia

Mitra

McEwan

Irons

Sanghera

(2007). Cultural differences in shame-focused attitudes towards mental health problems in Asian and Non-Asian student women. Mental Health, Religion & Culture, 10(2), 127–141. https://doi.org/10.1080/13694670500415124

17.

Goffman

(2009). Stigma: Notes on the management of spoiled identity. Simon & Schuster.

18.

Hipp

J. R.

Bauer

D. J.

(2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11(1), 36–53. https://doi.org/10.1037/1082-989X.11.1.36

19.

R. W. H.

Chang

W. C.

Kwong

V. W. Y.

Lau

E. S. K.

Chan

G. H. K.

Jim

O. T. T.

Hui

C. L. M.

Tang

J. Y. M.

Lee

E. H. M.

Chen

E. Y. H.

Sham

P. C

. (2018). Prediction of self-stigma in early psychosis: 3-year follow-up of the randomized-controlled trial on extended early intervention. Schizophrenia Research, 195, 463–468. https://doi.org/10.1016/j.schres.2017.09.004

20.

Holder

S. M.

Peterson

E. R.

Stephens

Crandall

L. A.

(2019). Stigma in mental health at the macro and micro levels: Implications for mental health consumers and professionals. Community Mental Health Journal, 55(3), 369–374. https://doi.org/10.1007/s10597-018-0308-y

21.

Hutchinson

(2025). Stigma and shame. In Schramme

Walker

(Eds.), Handbook of the Philosophy of Medicine (pp. 1–19). Springer Netherlands. https://doi.org/10.1007/978-94-017-8706-2_93-1

22.

Khemani

M. C.

Premarajan

K. C.

Menon

Olickal

J. J.

Vijayageetha

Chinnakali

(2020). Pathways to care among patients with severe mental disorders attending a tertiary health-care facility in Puducherry, South India. Indian Journal of Psychiatry, 62(6), 664–669. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_512_19

23.

King

D. D.

Lopiano

Fattoracci

E. S. M.

(2024). A stigma-conscious framework for resilience and posttraumatic change. American Psychologist, 79(8), 1155–1170. https://doi.org/10.1037/amp0001330

24.

Kroska

Harkness

S. K.

(2021). Information vs. inspiration: Evaluating the effectiveness of mental illness stigma-reduction messages. Social Science Research, 96, Article 102543. https://doi.org/10.1016/j.ssresearch.2021.102543

25.

Loch

A. A.

Guarniero

F. B.

Lawson

F. L.

Hengartner

M. P.

Rössler

Gattaz

W. F.

Wang

Y.-P.

(2013). Stigma toward schizophrenia: Do all psychiatrists behave the same? Latent profile analysis of a national sample of psychiatrists in Brazil. BMC Psychiatry, 13(1), Article 92. https://doi.org/10.1186/1471-244X-13-92

26.

Loch

A. A.

Wang

Y.-P.

Guarniero

F. B.

Lawson

F. L.

Hengartner

M. P.

Rössler

Gattaz

W. F.

(2014). Patterns of stigma toward schizophrenia among the general population: A latent profile analysis. International Journal of Social Psychiatry, 60(6), 595–605. https://doi.org/10.1177/0020764013507248

27.

Chen

Bai

Chen

Gao

Yuan

Hou

Gao

(2025). Worldwide changes in self-stigma among people with mental illness from 2005 to 2023: A cross-temporal meta-analysis and systematic review. International Psychogeriatrics, Article 100106. Advance online publication. https://doi.org/10.1016/j.inpsyc.2025.100106

28.

Mackenzie

C. S.

Knox

V. J.

Gekoski

W. L.

Macaulay

H. L.

(2004). An adaptation and extension of the Attitudes Toward Seeking Professional Psychological Help Scale. Journal of Applied Social Psychology, 34(11), 2410–2433. https://doi.org/10.1111/j.1559-1816.2004.tb01984.x

29.

Moitra

Owens

Hailemariam

Wilson

K. S.

Mensa-Kwao

Gonese

Kamamia

C. K.

White

Young

D. M.

Collins

P. Y.

(2023). Global mental health: Where we are and where we are going. Current Psychiatry Reports, 25(7), 301–311. https://doi.org/10.1007/s11920-023-01426-8

30.

Post

Buchta

Kemmler

Pardeller

Frajo-Apor

Hofer

(2021). Resilience Predicts self-stigma and stigma resistance in stabilized patients with bipolar I disorder. Frontiers in Psychiatry, 12, Article 678807. https://doi.org/10.3389/fpsyt.2021.678807

31.

Poudel

Timalsina

(2025). Factors associated with resilience among patients with end-stage kidney disease receiving hemodialysis in a teaching hospital: A cross-sectional study. BMC Nephrology, 26(1), Article 99. https://doi.org/10.1186/s12882-025-04008-3

32.

Raghavan

Brown

Horne

Kumar

Parameswaran

Ali

A. B.

Raghu

Wilson

Svirydzenka

Venkateswaran

Kumar

Kamal

S. R.

Barrett

Dasan

Varma

Banu

(2023). Stigma and mental health problems in an Indian context. Perceptions of people with mental disorders in urban, rural and tribal areas of Kerala. International Journal of Social Psychiatry, 69(2), 362–369. https://doi.org/10.1177/00207640221091187

33.

Sanchez

Grzenda

Varias

Widge

A. S.

Carpenter

L. L.

McDonald

W. M.

Nemeroff

C. B.

Kalin

N. H.

Martin

Tohen

Filippou-Frye

Ramsey

Linos

Mangurian

Rodriguez

C. I.

(2020). Social media recruitment for mental health research: A systematic review. Comprehensive Psychiatry, 103, Article 152197. https://doi.org/10.1016/j.comppsych.2020.152197

34.

Shahwan

Lau

J. H.

Goh

C. M. J.

Ong

W. J.

Tan

G. T. H.

Kwok

K. W.

Samari

Lee

Y. Y.

Teh

W. L.

Seet

Chang

Chong

S. A.

Subramaniam

(2020). The potential impact of an anti-stigma intervention on mental health help-seeking attitudes among university students. BMC Psychiatry, 20(1), Article 562. https://doi.org/10.1186/s12888-020-02960-y

35.

Sharif-Nia

Sánchez-Teruel

Sivarajan Froelicher

Hejazi

Hosseini

Khoshnavay Fomani

Moshtagh

Mollaei

Goudarzian

A. H.

Babaei

(2024). Connor-Davidson Resilience Scale: A systematic review of psychometric properties using the COSMIN. Annals of Medicine and Surgery, 86(5), 2976–2991. https://doi.org/10.1097/MS9.0000000000001968

36.

Spurk

Hirschi

Wang

Valero

Kauffeld

(2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, Article 103445. https://doi.org/10.1016/j.jvb.2020.103445

37.

Sum

M. Y.

Wong

C. T. W.

Chu

S. T.

Lee

A. H. T.

Chen

E. Y. H.

Chan

S. K. W.

(2024). Systematic review and meta-analysis of internalised stigma and stigma resistance in patients with psychosis: The impact of individualism-collectivism culture and other individual factors. International Journal of Social Psychiatry, 70(4), 639–652. https://doi.org/10.1177/00207640231216924

38.

Vaishnav

Javed

Gupta

Kumar

Vaishnav

Kumar

Salih

Levounis

Alkhoori

Luguercho

Soghoyan

Moore

Lakra

Aigner

Wancata

Ismayilova

Islam

Md. A.

Da Silva

A. G.

, . . . Ashurov

(2023). Stigma towards mental illness in Asian nations and low-and-middle-income countries, and comparison with high-income countries: A literature review and practice implications. Indian Journal of Psychiatry, 65(10), 995–1011. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_667_23

39.

Van Herpen

M. H

. (2023). The rise of the shame society: America’s change from a guilt culture into a shame culture (1st ed.). Bloomsbury Publishing.

40.

Walsh

D. A. B.

Foster

J. L. H.

(2021). A call to action. A critical review of mental health related anti-stigma campaigns. Frontiers in Public Health, 8, Article 569539. https://doi.org/10.3389/fpubh.2020.569539

41.

Weaver

L. J.

Karasz

Muralidhar

Jaykrishna

Krupp

Madhivanan

(2023). Will increasing access to mental health treatment close India’s mental health gap? SSM–Mental Health, 3, Article 100184. https://doi.org/10.1016/j.ssmmh.2022.100184

42.

Weller

B. E.

Bowen

N. K.

Faubert

S. J

. (2020). Latent class analysis: A guide to best practice. Journal of Black Psychology, 46(4), 287–311. https://doi.org/10.1177/0095798420930932

43.

Zieger

Mungee

Schomerus

T. T.

Dettling

Angermeyer

Hahn

(2016). Perceived stigma of mental illness: A comparison between two metropolitan cities in India. Indian Journal of Psychiatry, 58(4), Article 432. https://doi.org/10.4103/0019-5545.196706

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.04 MB