Outcomes reported in evaluations of programs designed to improve health in Indigenous people
Bibliographic record
Abstract
OBJECTIVE: To assess the outcomes reported and measured in evaluations of complex health interventions in Indigenous communities. DATA SOURCES: We searched all publications indexed in MEDLINE, PreMEDLINE, EMBASE, PsycINFO, EconLit, and CINAHL until January 2020 and reference lists from included papers were hand-searched for additional articles. STUDY DESIGN: Systematic review. DATA COLLECTION/EXTRACTION METHODS: We included all primary studies, published in peer-reviewed journals, where the main objective was to evaluate a complex health intervention developed specifically for an Indigenous community residing in a high-income country. Only studies published in English were included. Quantitative and qualitative data were extracted and summarized. PRINCIPAL FINDINGS: Of the 3523 publications retrieved, 62 evaluation studies were included from Australia, the United States, Canada, and New Zealand. Most studies involved less than 100 participants and were mainly adults. We identified outcomes across 13 domains: clinical, behavioral, process-related, economic, quality of life, knowledge/awareness, social, empowerment, access, environmental, attitude, trust, and community. Evaluations using quantitative methods primarily measured outcomes from the clinical and behavioral domains, while the outcomes reported in the qualitative studies were mostly from the process-related and empowerment domains. CONCLUSION: The outcomes from qualitative evaluations, which better reflect the impact of the intervention on participant health, remain different from the outcomes routinely measured in quantitative evaluations. Measuring the outcomes from qualitative evaluations alongside outcomes from quantitative evaluations could result in more relevant evaluations to inform decision making in Indigenous health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".