The role of moral explanations and structural inequalities in experiences of mental illness stigma in Northern Minas Gerais, Brazil
Bibliographic record
Abstract
The process of stigmatization within different cultural contexts has long been viewed as essential in understanding the course and outcomes of mental illness. However, little research has examined which cultural constructs and categories are used to explain mental illness, and how they contribute to the way people with mental illness experience stigma and social exclusion, as well as how these beliefs affect healthcare practices. This study examines meanings ascribed to mental illness and experiences of stigma among four groups in urban settings of Minas Gerais, Brazil: persons with mental illness; their families; members of the lay public; and health professionals working at an alternative community-based psychosocial treatment service or a local university hospital. Qualitative methods, including semi-structured interviews and participant observation, were conducted with a purposive sample of 72 participants. Data were analyzed through content analysis. The findings suggest that stigma and discrimination are intrinsically rooted in a systemic process of social exclusion generated by meanings ascribed to mental illness and the structural vulnerabilities of the mental healthcare system. The findings further suggest that structural inequality is a powerful factor behind lay concepts of mental illness and that this is particularly harmful because it reinforces personal blame attributions instead of addressing the hidden structural forces that contribute to mental illness. The study highlights the subtle interrelations between cultural beliefs and structural vulnerabilities that should be addressed in mental health policy in order to diminish the effects of stigma on people with mental illnesses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".