Reviewing Health Service and Program Evaluations in Indigenous Contexts: A Systematic Review
Bibliographic record
Abstract
This study systematically reviewed evidence regarding health program and service evaluations in Indigenous contexts. Following the PRISMA guidelines and combining terms for ‘Indigenous populations’ and ‘health programs and services’. Eight principles emerged: Principle 1: Adopting Indigenous led or co-led approaches is vital to balance power relationships by prioritizing self-determination, Principle 2: Evaluation team should include local Indigenous community members, Principle 3: Indigenous community knowledge and practice should be foundational, Principle 4: Evaluations must be responsive and flexible to meet the needs of the local community, Principle 5: Evaluations should respect and adhere to local Indigenous protocols, culture, wisdom and language, Principle 6: Evaluations should emphasize reciprocity, shared learnings and capacity building, Principle 7: It is important to build strong relationships and trust between and within researcher teams, evaluators and communities, and Principle 8: The evaluation team must acknowledge community capacity and resources by investing in time and relationships.
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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 | 0.063 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".