Indigenous Elders and Academic Leaders' Perspectives on Training Residents to Work With Indigenous Populations: A Constructivist Grounded Theory Study
Bibliographic record
Abstract
INTRODUCTION: Indigenous populations continue to experience healthcare inequities due in large part to the ongoing impacts of colonization. A literature review revealed that postgraduate educational interventions can help reduce inequities, including cultural safety training and formal Indigenous training curricula for residents. However, it was noted that these interventions are not part of many training programmes, and the voices of Indigenous communities were often absent in the creation of cultural safety curricula. This study aimed to explore ways to improve postgraduate training and better prepare residents to care for Indigenous patients from the perspectives of Indigenous elders and academic leaders. METHODS: A constructivist grounded theory approach was employed to organize and interpret the data. Interviews employed conversational methods, including open-ended, semistructured interview questions, to facilitate conversation with the study participants. Theoretical sampling was used to recruit Indigenous elders (n = 4) and academic leaders (n = 16) across two provinces in Canada. Data were transcribed and subsequently entered as verbatim transcripts to facilitate data analysis. RESULTS: The analysis revealed the following themes with numerous subthemes: (1) Helping faculty on their journey of truth, reconciliation and unlearning; (2) leading by example and with humility; (3) compensation for Indigenous curriculum-related activities acknowledges the value of Indigenous colleagues; and (4) clinical practice is enriched by the integration of Indigenous approaches. DISCUSSION: Residency programmes should ensure that the necessary supports are available for faculty and academic leaders who aim to develop and implement Indigenous curricula. Providing resources to academic leaders is vital for the successful implementation of Indigenous 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 | 0.026 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".