Stigma toward mental and physical illness: attitudes of healthcare professionals, healthcare students and the general public in Pakistan
Bibliographic record
Abstract
BACKGROUND: The evidence base for stigma in mental health largely originates from high-income countries. AIMS: This study from Pakistan aimed to address the gap in literature on stigma from low- and middle-income countries. METHOD: This cross-sectional study surveyed 1470 adults from Karachi, Pakistan. Participants from three groups (healthcare professionals, healthcare students and the general public) completed the adapted Bogardus Social Distance Scale (SDS) as a measure of stigma. RESULTS: All three groups reported higher scores of stigma toward mental disorders compared with physical disorders. SDS scores for mental illness in the general public were significantly higher than in healthcare students (mean difference (MD) 6.93, 95% CI 5.45-8.45, P < 0.001) and healthcare professionals (MD 6.93, 95% CI 5.48-8.38, P < 0.001). However, SDS scores between healthcare students and healthcare professionals were not significantly different (MD 0.003, 95% CI -1.14-1.14, P > 0.99). Being female was associated with lower stigma scores and being over the age of 30 years was associated with higher stigma scores. CONCLUSIONS: Stigma campaigns in Pakistan need to target the general population. However, evidence of negative attitudes toward mental illness in healthcare students and healthcare professionals supports the need for stronger emphasis on psychiatric education within undergraduate and postgraduate training in Pakistan.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".