Social Accountability of Medical Schools to Indigenous Students: A Critical Qualitative Analysis of Trainee Experiences in Western Canadian Institutions
Bibliographic record
Abstract
As medical schools increasingly aspire towards diverse student bodies, external directives such as the Truth and Reconciliation Commission (TRC) of Canada’s Calls to Action may guide efforts to achieve Indigenous inclusion. This research focuses primarily on call to action number 23, in which all levels of government (and presumably bodies funded by these) are compelled to increase the number of Indigenous health care professionals in Canada. This study begins by noting that simply increasing the number of Indigenous medical students via recruitment would be an unsatisfactory response; dilemmas around retention challenge institutions to grapple with how inclusion of diverse students may be accomplished, and preparation for successful careers ensured. In order to address these challenges, this qualitative research gains insight from three Indigenous medical students and four Indigenous physicians, exploring their time as medical students and in particular the barriers and facilitators to success they experienced as learners. Data was collected through semi-structured, in-depth interviews carried out with each participant, and were analyzed using coding strategies informed by grounded theory. The findings indicate participants faced barriers to success due to the colonial legacy of institutions, including structural violence, reported to most prominently be experienced as racism. Institutional efforts to diversify student bodies were characterized as superficial if focused narrowly on simply increasing the numbers of admitted Indigenous trainees, without institutional changes for inclusion. Lack of broader efforts for inclusion played out in personal experiences among the current and former trainees, for instance in fearing judgement from others within medical schooling should one self-identify as Indigenous. Success was found in connecting with other Indigenous people in medical schools. Physical spaces on campuses were noted to facilitate such connection. Important to participants were efforts to increase the capacity to address racism towards all people within medical schools. As medical institutions aspire to acknowledge historic wrongs through Indigenous inclusion, they contribute to the underlying intention of closing gaps in health outcomes between populations in Canada. This opens opportunities for inclusion of Indigenous ways of healing and understandings of wellness as central to achieving health equity.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".