A systematic review of population health interventions and Scheduled Tribes in India
Bibliographic record
Abstract
BACKGROUND: Despite India's recent economic growth, health and human development indicators of Scheduled Tribes (ST) or Adivasi (India's indigenous populations) lag behind national averages. The aim of this review was to identify the public health interventions or components of these interventions that are effective in reducing morbidity or mortality rates and reducing risks of ill health among ST populations in India, in order to inform policy and to identify important research gaps. METHODS: We systematically searched and assessed peer-reviewed literature on evaluations or intervention studies of a population health intervention undertaken with an ST population or in a tribal area, with a population health outcome(s), and involving primary data collection. RESULTS: The evidence compiled in this review revealed three issues that promote effective public health interventions with STs: (1) to develop and implement interventions that are low-cost, give rapid results and can be easily administered, (2): a multi-pronged approach, and (3): involve ST populations in the intervention. CONCLUSION: While there is a growing body of knowledge on the health needs of STs, there is a paucity of data on how we can address these needs. We provide suggestions on how to undertake future population health intervention research with ST populations and offer priority research avenues that will help to address our knowledge gap in this area.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.016 |
| 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) | 0.005 | 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".