Validation of the Opening Minds Scale and patterns of stigma in Chilean primary health care
Bibliographic record
Abstract
Stigma toward people with mental health problems (MHP) in primary health care (PHC) settings is an important public health challenge. Research on stigma toward MHP is relatively scarce in Chile and Latin America, as are instruments to measure stigma that are validated for use there. The present study aims to validate the Opening Minds Scale for Health Care Professionals (OMS-HC) among staff and providers in public Chilean PHC clinics, and examine differences in stigma by sociodemographic characteristics.803 participants from 34 PHC clinics answered a self-administered questionnaire. Confirmatory factor analysis was completed. Average 15-item OMS-HC scores were calculated, and means were compared via t-test or ANOVA to identify group differences. Correlations of OMS-HC scores with other commonly used stigma scores were calculated to evaluate construct validity.The 3-factor OMS-HC structure was confirmed in this population. The average OMS-HC (α = 0.69) score was 34.55 (theoretical range 15-75). Significantly lower (less stigmatizing) mean OMS-HC scores were found in those with additional training and/or personal experience with MHP.The validated, Spanish version of OMS-HC can be of use to further research stigma toward MHP in Chile and Latin America, advancing awareness and inspiring interventions to reduce stigma in the future.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".