The Health of Indigenous Populations in South Asia: A Critical Review in a Critical Time
Bibliographic record
Abstract
Despite South Asia's promising social inclusion processes, staggering social and health inequalities leave indigenous populations largely excluded. Marginalization in the South Asian polity, unequal power relations, and poor policy responses deter Adivasi populations' rights and opportunities for health gains and dignity. The ongoing COVID-19 pandemic is likely to result in a disproportionate share of infections and deaths among the Adivasis, given poor social conditions and exclusions. Poor health of indigenous people, inequalities between indigenous and non-indigenous groups, and failures in enforcing constitutional and legal provisions to reclaim indigenous land and cultural identity herald deeper structural and political fractures. This article unravels health inequalities between the Adivasis and non-Adivasi populations in their social context based on a critical review of secondary sources. We call for intersectoral policies and integrated health care services to address systemic inequalities, discrimination, power asymmetries, and consequent poor health outcomes. The current COVID-19 pandemic should be viewed as a window to pursue real change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".