‘First in family’ experiences in a Canadian medical school: A critically reflexive study
Bibliographic record
Abstract
BACKGROUND: Medical students from affluent and highly educated backgrounds remain overrepresented in Canadian medical schools despite widespread efforts to improve diversity. Little is known of the medical school experiences of students who are first in their family (FiF) to attend university. Drawing on Bourdieu and a critically reflexive lens, this study explored the experiences of FiF students in a Canadian medical school to better understand the ways in which the medical school environment can be exclusive and inequitable to underrepresented students. METHODS: We interviewed 17 medical students who self-identified as being FiF to attend university. Utilising theoretical sampling, we also interviewed five students who identified as being from medical families to test our emerging theoretical framework. Participants were asked to discuss what 'first in family' meant to them, their journey into medical school and their experiences at medical school. Bourdieu's theories and concepts were used as sensitising concepts to explore the data. RESULTS: FiF students discussed the implicit messages they received about who belongs in medical school, challenges in shifting from their pre-medical lives to a medical identity and competing with peers for residency programmes. They reflected on the advantages they perceived they had over their fellow students due to their less 'typical' social backgrounds. CONCLUSION: While medical schools continue to make strides when it comes to increasing diversity, inclusivity and equity require increased attention. Our findings highlight the ongoing need for structural and cultural change at admissions and beyond-change that recognises the much-needed presence and perspectives that underrepresented medical students, including those who are FiF, bring to medical education and healthcare. Engaging in critical reflexivity represents a key way that medical schools can continue to address issues of equity, diversity and inclusion.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.051 | 0.028 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".