[Interventions to improve access to health services by indigenous peoples in the Americas].
Bibliographic record
Abstract
OBJECTIVE: Synthesize evidence on effectiveness of interventions designed to improve access to health services by indigenous populations. METHODS: Review of systematic reviews published as of July 2015, selecting and analyzing only studies in the Region of the Americas. The bibliographic search encompassed MEDLINE, Lilacs, SciELO, EMBASE, DARE, HTA, The Cochrane Library, and organization websites. Two independent reviewers selected studies and analyzed their methodological quality. A narrative summary of the results was produced. RESULTS: Twenty-two reviews met the inclusion criteria. All selected studies were conducted in Canada and the United States of America. The majority of the interventions were preventive, to surmount geographical barriers, increase use of effective measures, develop human resources, and improve people's skills or willingness to seek care. Topics included pregnancy, cardiovascular risk factors, diabetes, substance abuse, child development, cancer, mental health, oral health, and injuries. Some interventions showed effectiveness with moderate or high quality studies: educational strategies to prevent depression, interventions to prevent childhood caries, and multicomponent programs to promote use of child safety seats. In general, results for chronic non-communicable diseases were negative or inconsistent. CONCLUSIONS: Interventions do exist that have potential for producing positive effects on access to health services by indigenous populations in the Americas, but available studies are limited to Canada and the U.S. There is a significant research gap on the topic in Latin America and the Caribbean.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".