Beyond form and functioning: Understanding how contextual factors influence village health committees in northern India
Bibliographic record
Abstract
Health committees are a common strategy to foster community participation in health. Efforts to strengthen committees often focus on technical inputs to improve committee form (e.g. representative membership) and functioning (e.g. meeting procedures). However, porous and interconnected contextual spheres also mediate committee effectiveness. Using a framework for contextual analysis, we explored the contextual features that facilitated or hindered Village Health, Sanitation and Nutrition Committee (VHSNC) functionality in rural north India. We conducted interviews (n = 74), focus groups (n = 18) and observation over 1.5 years. Thematic content analysis enabled the identification and grouping of themes, and detailed exploration of sub-themes. While the intervention succeeded in strengthening committee form and functioning, participant accounts illuminated the different ways in which contextual influences impinged on VHSNC efficacy. Women and marginalized groups navigated social hierarchies that curtailed their ability to assert themselves in the presence of men and powerful local families. These dynamics were not static and unchanging, illustrated by pre-existing cross-caste problem solving, and the committee's creation of opportunities for the careful violation of social norms. Resource and capacity deficits in government services limited opportunities to build relationships between health system actors and committee members and engendered mistrust of government institutions. Fragmented administrative accountability left committee members bearing responsibility for improving local health without access to stakeholders who could support or respond to their efforts. The committee's narrow authority was at odds with widespread community needs, and committee members struggled to involve diverse government services across the health, sanitation, and nutrition sectors. Multiple parallel systems (political decentralization, media and other village groups) presented opportunities to create more enabling VHSNC contexts, although the potential to harness these opportunities was largely unmet. This study highlights the urgent need for supportive contexts in which people can not only participate in health committees, but also access the power and resources needed to bring about actual improvements to their health and wellbeing.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".