Burden and determinants of anemia among tribal women in Jammu and Kashmir: A systematic review and meta-analysis.
Bibliographic record
Abstract
Anemia remains a major public health problem affecting women of reproductive age, particularly in low- and middle-income countries. Tribal populations are among the most vulnerable groups due to socioeconomic disadvantage, limited healthcare access, and poor nutritional status. In the Union Territory of Jammu and Kashmir, tribal communities such as the Gujjar and Bakarwal inhabit geographically remote regions where health services and nutritional resources may be limited. Although several individual studies have reported anemia among tribal women in this region, the overall burden and associated determinants have not been systematically synthesized. The present systematic review and meta-analysis aimed to estimate the pooled prevalence of anemia and identify key determinants among tribal women in Jammu and Kashmir. A comprehensive literature search was conducted across major electronic databases including PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar for studies published up to Decmeber 2025. Observational studies reporting anemia prevalence or determinants among tribal women were included. Fourteen studies met the eligibility criteria and were included in the qualitative synthesis, while twelve studies provided sufficient data for meta-analysis. The pooled prevalence of anemia among tribal women was estimated to be 54.8% (95% CI: 47.2%–62.3%), indicating a substantial burden in this population. Poor dietary intake, low socioeconomic status, limited access to healthcare services, inadequate antenatal care utilization, and high parity were the most frequently reported determinants. The findings highlight a considerable burden of anemia among tribal women in Jammu and Kashmir and emphasize the need for targeted public health interventions focusing on nutritional improvement, maternal healthcare services, and improved accessibility of healthcare in remote tribal areas.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".