Building community involvement in cross-cultural Indigenous health programs
Bibliographic record
Abstract
OBJECTIVE: To gain preliminary knowledge about issues identified by Native health investigators who would encourage greater community involvement in Indigenous health programs and research in Canada, Pacific Rim, and the United States. DESIGN: A pilot/feasibility study, August 2001-April 2002. SETTING: Indigenous health agencies and institutions in New Zealand, Australia, Canada, and the United States. PARTICIPANTS: Thirty-six health professionals from rural and urban health centers participated, which resulted in 10 group and four individual interviews. Subjects included program managers, clinical physicians, and health researchers. Approximately 58% of the subjects self-identified as Indigenous. RESULTS: Three overarching themes emerged from the interview data: (i) integration of cultural values of family and community into health provision; (ii) emphasis on health education and prevention programs for Indigenous youth; and (iii) indigenous recognition and self-determination in health delivery and research. CONCLUSIONS: To improve and promote community involvement in primary health programs and services for Indigenous people involves a long-term social and political commitment to health protection on a national and an international level, as well as the understanding that research methodologies and health interventions must explicitly involve culturally appropriate values and behaviors that are implemented by Indigenous people.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".