Systematic Review of Physical Activity Interventions Implemented with American Indian and Alaska Native Populations in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: To describe physical activity (PA) interventions implemented in American Indian/Alaska Native (AI/AN) populations in the United States and Canada. DATA SOURCES: MEDLINE, PubMed, ERIC, and Sociological Abstracts were used to identify peer-reviewed journal articles. Dissertation abstracts, Web sites, and conference proceedings were searched to identify descriptions within the gray literature from 1986 to 2006. STUDY INCLUSION AND EXCLUSION CRITERIA: The target population had to be described as AI/ AN, aboriginal, native Hawaiian, and/or native U.S. Samoan. PA interventions among indigenous populations of Latin America were not included. DATA EXTRACTION: Descriptions of 64 different AI/AN PA interventions (28 peer-reviewed journal articles and 36 in the gray literature) were identified. DATA SYNTHESIS: Data were synthesized by geographic region, intervention strategy, target audience, activities, and sustainability. RESULTS: Most interventions were conducted in the southwest United States (35.4%), in reservation communities (72%), and among participants 18 years and younger (57.8%). Forty-one percent of the 27 interventions with evaluation components reported significant changes in health, behavior, or knowledge. CONCLUSIONS: Effective AI/AN PA interventions demonstrated impact on individual health and community resources. Program sustainability was linked to locally trained personnel, local leadership, and stable funding. Culturally acceptable and scientifically sound evaluation methods that can be implemented by local personnel are needed to assess the health and social impact of many long-running AI/AN PA interventions.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".