Navigating the burden of proof and responsibility: A narrative inquiry into Indigenous medical learners' experiences
Bibliographic record
Abstract
INTRODUCTION: Many medical schools have well-established admission pathways and programming to support Indigenous medical workforce development. Ideally, these efforts should contribute to attracting highly qualified Indigenous applicants which, in turn, may improve accessible, quality care for Indigenous people. However, it is difficult to evolve and tailor these approaches without a situated understanding of Indigenous learners' experiences. In this paper, we focus on the Canadian context, sharing Indigenous learners' stories about their journey towards and throughout medical training. METHODS: The conceptual underpinnings of narrative inquiry and key principles from Indigenous methodologies were drawn upon throughout both data collection and analysis. Participants were Indigenous learners (medical students and residents) and a recently graduated physician (n = 5) from one Canadian medical school. Both spoken (formal recorded interviews) and visual (photographs) texts were used to make meaning of participants' experiences. RESULTS: Participants' experiences during medical training showed a striking resemblance at three points in their transition to, and progression through, medical education: preparing for and applying to medical school, completing undergraduate medical training and determining specialty choice. Participants' stories revealed a tug-of-war between their identities as an Indigenous person and as a medical trainee, with these tensions sometimes compromising their perceived sense of belonging within both Indigenous and academic circles, creating, at times, a heavy burden to shoulder. CONCLUSION: Meaningful representation of Indigenous people in the medical workforce is about more than training additional health care providers; it requires understanding Indigenous learners and recently graduated physicians' experiences as they enter and navigate the medical profession. By amplifying their voices, we stand to gain a more holistic representation of the factors that contribute to and potentially impede the recruitment and retention of Indigenous people into the medical profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".