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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Stigma related to mental illness (and its treatment) is prevalent worldwide. This stigma could be at the structural or organizational level, societal level (interpersonal stigma), and the individual level (internalized stigma). Vulnerable populations, for example, gender minorities, children, adolescents, and geriatric populations, are more prone to stigma. The magnitude of stigma and its negative influence is determined by socio-cultural factors and macro (mental health policies, programs) or micro-level factors (societal views, health sectors, or individuals' attitudes towards mentally ill persons). Mental health stigma is associated with more serious psychological problems among the victims, reduced access to mental health care, poor adherence to treatment, and unfavorable outcomes. Although various nationwide and well-established anti-stigma interventions/campaigns exist in high-income countries (HICs) with favorable outcomes, a comprehensive synthesis of literature from the Low- and Middle-Income Countries (LMICs), more so from the Asian continent is lacking. The lack of such literature impedes growth in stigma-related research, including developing anti-stigma interventions. Aim: To synthesize the available mental health stigma literature from Asia and LMICs and compare them on the mental health stigma, anti-stigma interventions, and the effectiveness of such interventions from HICs. Materials and Methods: PubMed and Google Scholar databases were screened using the following search terms: stigma, prejudice, discrimination, stereotype, perceived stigma, associate stigma (for Stigma), mental health, mental illness, mental disorder psychiatric* (for mental health), and low-and-middle-income countries, LMICs, High-income countries, and Asia, South Asian Association for Regional Cooperation/SAARC (for countries of interest). Bibliographic and grey literature were also performed to obtain the relevant records. Results: The anti-stigma interventions in Asia nations and LMICs are generalized (vs. disorder specific), population-based (vs. specific groups, such as patients, caregivers, and health professionals), mostly educative (vs. contact-based or attitude and behavioral-based programs), and lacking in long-term effectiveness data. Government, international/national bodies, professional organizations, and mental health professionals can play a crucial in addressing mental health stigma. Conclusion: There is a need for a multi-modal intervention and multi-sectoral coordination to mitigate the mental health stigma. Greater research (nationwide surveys, cultural determinants of stigma, culture-specific anti-stigma interventions) in this area is required.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it