Reducing inequalities in health and access to health care in a rural Indian community: an India-Canada collaborative action research project
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Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle