Evaluation of a longitudinal Indigenous health elective in family medicine
Bibliographic record
Abstract
Background: In response to the Truth and Reconciliation Commission of Canada Calls to Action 22 to 24 around health, the Department of Family Medicine at the University of Calgary piloted a novel Indigenous Health Longitudinal Elective (IHLE) to give first year residents longitudinal experiences in Indigenous healthcare environments. The purpose of this evaluation was to capture the successful qualities and identify areas for improvements to ensure feasibility of the IHLE pilot program. Methods: Between November 2022 and April 2023, semi-structured interviews were completed with seven participants of the IHLE and included a mix of residents, preceptors, and clinic staff members. Qualitative thematic analysis was used to gain an in-depth understanding of the IHLE program experiences of all participants. Results: Benefits of the IHLE program include a deeper understanding of the values and priorities critical to working in healthcare with Indigenous peoples in Southern Alberta. Areas for improvement include clarity around IHLE program structure; clearly defining roles and responsibilities for preceptors; increased opportunities for reciprocity and relationality; and a deeper self-reflection process. Conclusion: Recommendations for future iterations of the IHLE include ensuring preceptors are trained and engaged, while providing residents more opportunities for relationality and peer debriefing. Results from this study may also help inform future Indigenous health programming in family medicine.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".