Reducing inequalities in health and access to health care in a rural Indian community: an India-Canada collaborative action research project
Bibliographic record
Abstract
BACKGROUND: Inadequate public action in vulnerable communities is a major constraint for the health of poor and marginalized groups in low and middle-income countries (LMICs). The south Indian state of Kerala, known for relatively equitable provision of public resources, is no exception to the marginalization of vulnerable communities. In Kerala, women's lives are constrained by gender-based inequalities and certain indigenous groups are marginalized such that their health and welfare lag behind other social groups. THE RESEARCH: The goal of this socially-engaged, action-research initiative was to reduce social inequalities in access to health care in a rural community. Specific objectives were: 1) design and implement a community-based health insurance scheme to reduce financial barriers to health care, 2) strengthen local governance in monitoring and evidence-based decision-making, and 3) develop an evidence base for appropriate health interventions. RESULTS AND OUTCOMES: Health and social inequities have been masked by Kerala's overall progress. Key findings illustrated large inequalities between different social groups. Particularly disadvantaged are lower-caste women and Paniyas (a marginalized indigenous group), for whom inequalities exist across education, employment status, landholdings, and health. The most vulnerable populations are the least likely to receive state support, which has broader implications for the entire country. A community based health solidarity scheme (SNEHA), under the leadership of local women, was developed and implemented yielding some benefits to health equity in the community-although inclusion of the Paniyas has been a challenge. THE PARTNERSHIP: The Canadian-Indian action research team has worked collaboratively for over a decade. An initial focus on surveys and data analysis has transformed into a focus on socially engaged, participatory action research. CHALLENGES AND SUCCESSES: Adapting to unanticipated external forces, maintaining a strong team in the rural village, retaining human resources capable of analyzing the data, and encouraging Paniya participation in the health insurance scheme were challenges. Successes were at least partially enabled by the length of the funding (this was a two-phase project over an eight year period).
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".