Indigenous Health Service Evaluation: Principles and Guidelines from a Provincial “Three Ribbon” Expert Panel
Bibliographic record
Abstract
Abstract: A group of Indigenous health and social service evaluators called the “Three Ribbon” panel came together in Toronto in 2015/16 with the goal of informing a set of evidence-based guidelines for urban Indigenous health and social service and program evaluation. The collective knowledge and experiences of the Three Ribbon panel was gathered through discussion circles and synthesized around the following areas: barriers to conducting Indigenous health and social service evaluation; decolonizing principles and protocols that support community self-determination and centralize Indigenous culture and worldviews; and guidelines to inform health and social service evaluation moving forward. The wisdom and contributions of the Three Ribbon Panel creates space for Indigenous worldviews, values, and beliefs within program evaluation practice and has important implications for evaluation research and application.
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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 | 0.529 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".