A Scoping Review of Indigenous Health Curricular Content in Graduate Medical Education
Bibliographic record
Abstract
Background: Graduate medical education is refocusing on the reconciliation process with Indigenous peoples and integrating Indigenous healing practices, cultural humility training, and courses on Indigenous health issues in their curricula. Physicians and all health care workers must be able to recognize, respect, and address the distinct health needs of all Indigenous peoples. Objective: The aim of this scoping review was to explore and describe what exists in the current literature on the impact and challenges associated with Indigenous curricula developed for resident physicians. Methods: The search was conducted using 9 bibliographic databases from inception until April 19, 2021. Two reviewers independently screened for inclusion using Covidence. Three reviewers extracted data and all 3 checked for completeness and accuracy. Results: Eleven reports were included. Our included reports consisted of qualitative research (n=2), commentaries (n=1), special articles (n=3), systematic reviews (n=1), innovation reports (n=1), published abstracts (n=1), and program evaluation papers (n=2). Findings are presented by 3 themes: (1) Misunderstandings and cultural bias toward Indigenous people; (2) Increasing community-driven Indigenous partnerships to create a safe environment; and (3) Challenges in implementing Indigenous health curricula. Conclusions: Themes identified related to Indigenous involvement, culturally competent care, common misconceptions about Indigenous peoples, as well as challenges and barriers to implementing Indigenous curricula for residency programs. A collaborative approach involving stakeholders with training in the community is a viable path forward. But comprehensive program evaluation, a source of stable funding, and further research focusing on effective Indigenous curricula for residents are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".