Stigma-related mental health knowledge and attitudes among primary health workers and community health volunteers in rural Kenya
Bibliographic record
Abstract
BACKGROUND: The study was conducted in rural Kenya and assessed stigma in health workers from primary health facilities. AIMS: This study compared variations in stigma-related mental health knowledge and attitudes between primary health workers (HWs) and community health volunteers (CHVs). METHODS: Participants ( n = 44 HWs and n = 60 CHVs) completed the self-report Mental Health Knowledge Schedule and the Reported and Intended Behavior Scale, along with sociodemographic questions. Multiple regression models were used to assess predictors of mental health knowledge and stigmatizing behaviors. RESULTS: HWs had significantly higher mean mental health knowledge scores than CHVs, p < .001, and significantly higher mean positive attitudes scores than CHVs, p = .042. When controlling for relevant covariates, higher positive attitudes was the only significant predictor of higher mental health knowledge, and self-rating of sense of belonging to the community and mental health knowledge remained the main predictors of positive attitudes. CONCLUSION: Results suggest that stigma-related mental health knowledge and attitudes are associated, and interventions should target these areas with health workers. There is scope for intervention to increase knowledge and positive attitudes for individuals who feel a strong sense of community belonging. Future studies should test feasible ways to reduce stigma in this population.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".