Evaluating transformative health leadership education for Indigenous health: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to improve structural competency and anti-racism education across health systems. Many leaders in health systems have the ability and responsibility to play a significant role in policy change and transforming healthcare delivery to address health inequities and injustices. The aim of this project was to evaluate a new health leadership Indigenous health course: PLUS4I. METHODS: A mixed methods design grounded in a pragmatic paradigm was used. Attendees to the first four cohorts (n=75) were sent an invitation to complete a survey evaluating their learning immediately after the completion of PLUS4I. We retrospectively collected self-efficacy ratings from participants who were also invited to participate in a semi-structured interview about their experience in PLUS4I. Descriptive statistical analysis was conducted for the quantitative assessment of the survey data. A qualitative descriptive approach to thematic analysis was used for the qualitative interview data. RESULTS: A total of 45 completed quantitative evaluations (n=45) were completed across all four cohorts. Paired t-tests were used to show pre-changes and post-changes in self-reported confidence on a 6-point Likert scale across four categories of activities. Improvements were seen in the ratings across all categories of activities, and all were statistically significant (p<0.001). Two overarching themes emerged from the qualitative analysis: breaking down previous knowledge and critical applications; building new knowledge and change-making competencies. The qualitative interviews (n=25) averaged 32:23 min, with 18 female (72%) and 7 male (28%) interview participants. CONCLUSION: Future work will support expansion of the PLUS4I course into other work environments and faculties, where the learning environment, structure and relevant Truth and Reconciliation Calls to Action may be different. This work responds to the urgent need to create systems-level change to address structural racism and implement high-quality Indigenous health and anti-racism education.
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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 | 0.047 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".