Burden of anemia among indigenous populations
Bibliographic record
Abstract
An international perspective of the magnitude of anemia in indigenous peoples is currently lacking. The present systematic review was performed to characterize the global prevalence, severity, and etiology of anemia in indigenous peoples by conducting a systematic search of original research published in English from 1996 to February 2010 using PubMed, Medline, and Embase. A total of 50 studies, representing the following 13 countries, met the inclusion criteria: Australia, Brazil, Canada, Guatemala, India, Kenya, Malaysia, Mexico, New Zealand, Sri Lanka, Tanzania, the United States, and Venezuela. Results indicate major deficiencies in the coverage and quality of anemia monitoring data for indigenous populations worldwide. The burden of anemia is overwhelmingly higher among indigenous groups compared to the general population and represents a moderate (20-39.9%) to severe (≥40%) public health problem. For the most part, the etiology of anemia is preventable and includes inadequate diet, poor living conditions, and high infection rates (i.e., malaria and intestinal parasites). A concerted global effort is needed to reduce the worldwide burden of anemia in these marginalized populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".