Stigma towards people with mental disorders: perceptions of devaluation and discrimination in a sample of Chilean workers
Bibliographic record
Abstract
Introduction. Mental disorders represent one of the main causes of disease burden in the adult population. Negative public attitudes and behaviors toward people with mental disorders negatively affect the treatment, recovery, and social inclusion of those affected. Chile laks surveys on workers that address this issue. Objective. To describe the perceptions of devaluation and discrimination towards people with mental disorders in a sample of Chilean workers. Method. A cross-sectional study was carried out with 1 516 workers in the formal sector of four regions of Chile (Metropolitan Region [RM], Bío Bio [VIII], Valparaíso [V] and Coquimbo [IV]). The perception of discrimination and devaluation was explored through a modified version of the The perceived Devaluation-Discrimination Scale (PDD) comprising 15 questions. The relationship of each question with sociodemographic variables (age, sex, years of study, and region) and type of economic activity was assessed. Results. The study found a high percentage of perceptions of devaluation and discrimination in most aspects considered, particularly those related to hiring a person who has been hospitalized due to a mental illness (85%), feeling sorry for people with severe mental illnesses (80%), and the unwillingness to marry a person with a mental illness (78%). Significant differences were observed in the opinions by sociodemographic variables and region of residence. Discussion and conclusion. The perception of Chilean workers has high levels of stigma towards people living with mental disorders. It is necessary and urgent to develop effective anti-stigma public policies to promote a more inclusive, tolerant society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".