Participant-reported effect of an Indigenous health continuing professional development initiative for specialists
Bibliographic record
Abstract
BACKGROUND: Health outcomes of Indigenous patients are impacted by culturally unsafe specialty care environments. The 'Educating for Equity (E4E)' program is a continuing professional development (CPD) intervention which incorporates skill-based teaching to improve Indigenous patient experiences and outcomes in healthcare interactions. METHODS: The E4E program was delivered to rheumatologists in two phases, each delivered as experiential learning workshops where participants engaged with and applied course content within an interactive format focusing on real-time feedback. The phase 1 workshop focused on skill development of E4E Framework concepts and principles. Phase 2 concentrated on building capacity for teaching of E4E content. Evaluation of the program's effectiveness was through longitudinal responses to the Social Cultural Confidence in Care Survey (SCCCS), self-reported strategies employed to address social issues and improve therapeutic relationships, engagement with teaching others, and satisfaction with the program. RESULTS: Two cohorts of participants have participated in the program (n = 24 Phase 1, n = 10 Phase 2). For participants completing both phases of training, statistically significant improvements were observed in exploring social factors with patients, gaining knowledge and skills related to cultural aspects of care, improved communication and relationship building, and reflections on held stereotypes. Strategies to address social issues and build therapeutic relationships remained consistent throughout participation, while the training enhanced exploration and confidence to ask about cultural and traditional practices, and stronger communication strategies for exploring beliefs, expectations, social barriers, and residential school impacts on health. Participants reported feeling prepared to teach Indigenous health concepts to others and subsequently lead teaching with residents, fellows, and allied health professionals. Satisfaction with the delivery and content of the workshops was high, and participants valued interactions with peers in learning. CONCLUSIONS: This CPD intervention had a beneficial impact on self-reported confidence and enhanced practice strategies to engage with Indigenous patients.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".