Responsibility to All Relations: Indigenous Evaluation of an AI/AN Public Health Workforce Development Program
Bibliographic record
Abstract
Aligning with the Western Door – Do Good Work, this article presents an evaluation of Urban Indian Health Institute’s Public Health Training Program (PHTP) by applying their Indigenous Evaluation Framework. The framework was created by and for urban Indigenous communities to conduct evaluation in a culturally rigorous way by reclaiming data for the well-being of the community, staying grounded in cultural knowledge systems, and utilizing Western science when needed. This article focuses on how the framework was applied to evaluate the PHTP’s effectiveness in strengthening the urban American Indian/Alaska Native (AI/AN) public health workforce. Findings contribute to the evidence base for culturally attuned approaches for improving the AI/AN workforce development in public health. The article makes the following recommendations for AI/AN workforce development programs: co-create training spaces with AI/AN communities, intentionally recruit and support AI/AN mentors, and use flexible workforce development frameworks that honour diverse pathways and definitions of success.
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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 | 0.068 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".