Labour upon labour: A best evidence medical education (BEME) meta-ethnography of underrepresented students’ experiences of medical school
Bibliographic record
Abstract
BACKGROUND: To date, advancing social justice in medical education has largely focused on recruitment and numerical representation of equity deserving groups. However, students recruited to diversify the profession often find themselves in an exclusionary environment that overlooks their multiple identities. The aim of this study was to illuminate underrepresented students' experiences of medical school to inform supports for these deserving students. METHODS: This meta-ethnography followed the seven steps developed by Noblit and Hare. Databases were systematically searched for peer-reviewed qualitative articles published between 2000 and late 2024 that described underrepresented students' experiences of medical school. Participants' and authors' concepts were collected and synthesized to develop new overarching concepts. RESULTS: Forty-nine qualitative studies met the inclusion criteria. Four overarching concepts were developed: working ten times harder; embodying the 'ideal' doctor; invisibility and hypervisibility; and survival strategies. CONCLUSIONS: This meta-ethnography deepens our understanding of historically excluded students' experiences of medical school. The default ideal of the (cis, white, wealthy, male, non-disabled) physician created an exclusionary environment that underrepresented students were forced to manage through additional cognitive, emotional, and embodied effort. This synthesis provides valuable strategies and considerations for medical educators and administrators committed to social justice initiatives grounded in underrepresented student voices.
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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 | 0.031 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".