Challenges in the sociocultural milieu of South Asia: a systematic review of community health workers
Bibliographic record
Abstract
INTRODUCTION: Community health workers (CHWs) connect patients in rural and remote communities to health service organizations. This diverse group of healthcare workers has helped improve healthcare access and outcomes and enhance the quality of life for people in hard-to-reach communities. However, CHWs face numerous challenges rooted in the sociocultural milieu of the region and country in which they reside. METHODS: This systematic review and qualitative meta-synthesis of 38 studies examines the sociocultural challenges that CHWs experience; it focuses on the unique history, geography, and sociocultural milieu of South Asia. RESULTS: This study found the following challenges that CHWs regularly face when working in communities: religious and cultural norms and practices, gender and biological sex, caste, and generation. All challenges in some way relate to one another and stem from the unique sociocultural milieu of South Asia, and the various subcultures that exist in this diverse region. CONCLUSION: This article presents important guidance for program planning and CHW deployment that reflects the sociocultural realities of practice. The findings of this investigation may serve as an essential resource for program planners and decision-makers in improving the effectiveness and reach of CHW programs.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".