Is Canada's Post-Graduate Medical Education Curricula Producing Physicians who can Provide Culturally Safe Care?
Bibliographic record
Abstract
Indigenous peoples living in Canada experience significant health inequities relative to non-Indigenous people, which stem largely from experiences of colonization, past and present. An important contributor to such inequities is the paucity of culturally safe healthcare available to Indigenous people. A lack of relevant educational experiences for healthcare professionals has been implicated in both creating culturally unsafe healthcare environments and in perpetuating these healthcare-related inequities (Guerra & Kurtz, 2017). The Truth and Reconciliation Commission of Canada (TRC, 2015) calls for improved cultural safety training for healthcare professionals treating Indigenous patients. Recently, post-graduate medical education training programs have shifted to a competency-based model (CBME) whereby specific learning objectives must be attained to graduate, compared to the historical time-based model (Iobst et al., 2010). However, it is unknown whether the CBME programs sufficiently fulfill the TRC calls to action pertaining to Indigenous health. The objective of this study is to determine the extent to which Canada’s CBME curricula provide cultural safety training regarding Indigenous health. An environmental scan of the publically available national CBME curricula will assess the content of the core portions of the training programs. A self-report mixed-methods survey will be distributed to medical residents at Queen’s University to determine the extent to which they perceive that such training is provided to them. This research aims to identify gaps in the CBME curricula pertaining to Indigenous health, so as to contribute to improved cultural safety training, and thus health equity, in the future.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".