Residency training programs to support residents working in First Nations, Inuit, and Métis communities
Bibliographic record
Abstract
BACKGROUND: To gain culturally appropriate awareness of First Nations, Inuit and/or Métis Health, research suggests that programs focus on sending more trainees to First Nations, Inuit and/or Métis communities Working within this context provides experiences and knowledge that build upon classroom education and support trainees' acquisition of skills to engage in culturally safe healthcare provision. This study examines residents' and faculty members' perceptions of how residency training programs can optimize First Nations, Inuit and/or Métis health training and support residents in gaining the knowledge, skills, and experiences for working in and with First Nations, Inuit and/or Métis communities. METHODS: A qualitative approach was used, guided by a relational lens for collecting data and a constructivist grounded theory for data interpretation. Theoretical sampling was used to recruit 35 participants from three main study sites across two western Canadian provinces. Recruitment, data collection, and analysis using constructivist grounded theory occurred concurrently to ensure appropriate depth of exploration. RESULTS: Our data analysis revealed five themes: Five themes were generated: Complexity of voluntourism as a concept; Diversity of knowledge representation required for developing curriculum; Effective models of care for First Nations, Inuit and/or Métis health; Essential traits that residents should have for working in First Nations, Inuit and/or Métis communities; and Building relationships and trust by engaging the community. CONCLUSIONS: First Nations, Inuit and/or Métis Health should be prioritized within Canadian postgraduate medical education. Equipping trainees to provide holistic care, immersing in and learning from First Nations, Inuit and/or Métis communities is essential for developing the next generation of clinicians and preceptors. We present educational recommendations for residency programs to optimize First Nations, Inuit and/or Métis health educational experiences and provide residents with skills to provide effective and culturally safe care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".