Impacts of Educating for Equity Workshop on Addressing Social Barriers of Type 2 Diabetes With Indigenous Patients
Bibliographic record
Abstract
INTRODUCTION: Health education about Indigenous populations in Canada (First Nations, Inuit, and Métis people) is one approach to enable health services to mitigate health disparities faced by Indigenous peoples related to a history of colonization and ongoing social inequities. This evaluation of a continuing medical education workshop, to enhance family physicians' clinical approach by including social and cultural dimensions within diabetes management, was conducted to determine whether participation in the workshop improved self-reported knowledge, skills, and confidence in working with Indigenous patients with type 2 diabetes. METHODS: The workshop, developed from rigorous national research with Indigenous patients, diabetes care physicians, and Indigenous health medical educators, was attended by 32 family physicians serving Indigenous populations on three sites in Northern Ontario. A same-day evaluation survey assessed participants' satisfaction with workshop content and delivery. Preworkshop and postworkshop surveys consisting of 5-point Likert and open-ended questions were administered 1 week before and 3 month after the workshop. Descriptive statistics and t test were performed to analyze Likert scale questions; thematic analysis was used to elicit and cluster themes from open-ended responses. RESULTS: Participants reported high satisfaction with all aspects of the workshop. Reporting improved understanding of socioeconomic (P = .002), psychosocial, and cultural factors (P = .001), participants also described adapting their clinical approach to more actively incorporating social and cultural factors and focusing on patient-centered care. DISCUSSION: The workshop was effective in shifting physician's self-reported knowledge, attitudes, and skills resulting in clinical approach modifications within social, psychosocial, and cultural domains for their Indigenous patients with diabetes.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".