Evaluating the Effectiveness of Indigenous Health Curricula: Validation and Application of the NOSM CAST Instrument
Bibliographic record
Abstract
OBJECTIVES In recent years, Indigenous health curricula have been integrated into medical education in response to international calls to improve Indigenous health care. Instruments to evaluate Indigenous health education are urgently needed. We set out to validate a tool to measure self-reported medical student preparedness to provide culturally safe care to Indigenous Peoples. We then applied the tool to evaluate the effectiveness of the Northern Ontario School of Medicine University's (NOSM U) Indigenous health curriculum. METHODS We conducted psychometric testing of a 46-item draft NOSM Cultural Competency and Safety Tool (CAST). Testing included principal components analysis, subscale and item analysis, and the use of paired sample t-tests to examine pre- and posttest change to measure learner outcomes. The NOSM CAST was transposed to create a retrospective pre–posttest survey with single-point-in-time scoring. RESULTS Respondents included five cohorts of first-year undergraduate medical students, with 305 of 320 participating (response rate of 95.3%). The validated survey subscales included knowledge, confidence/preparedness, attitudes, intentions for advocacy, antidiscrimination, and self-reflective practice, measured using 36 scale items. Cronbach's alpha showed good to excellent internal consistency for the scales ( α range = 0.82–0.91). Composite reliability values were acceptable. The pre–posttest analysis showed statistically significant increases on four scales: knowledge [ t(254) = 15.10, P < .001], confidence/preparedness [ t(254) = 15.85, P < .001], intentions for advocacy [ t(251) = 3.32, P = .001], and self-reflective practice [ t(254) = 8.04, P < .001]. The largest mean increases were for knowledge ( d = 1.07) and confidence/preparedness ( d = 1.15). CONCLUSIONS The NOSM CAST tracks student progress in Indigenous health curricula. NOSM U's classroom and immersion-based Indigenous health curriculum enhanced students’ self-reported preparedness for culturally safe care. NOSM CAST implemented together with an assessment of Indigenous patient experiences with the same learners constitutes a rigorous evaluation approach to Indigenous health curricula.
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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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".