Later is too late: Exploring student experiences of diversity and inclusion in medical school orientation
Bibliographic record
Abstract
While there is increasing effort among medical schools to recruit diverse students, there is a paucity of research into the unique experiences of these students during their transition to medicine. This study explored how experiences during medical school orientation influence students’ transition into the medical profession. Semi-structured interviews were conducted (April-August 2019) with 16 first-year Canadian medical students. We applied descriptive thematic analysis using a constant comparative approach. Verbatim transcripts were coded and analyzed to elucidate themes. Participants highlighted the importance of social orientation during their transition into medical school and noted experiencing complex social pressures during this time. They shared how incoming students were introduced to the dominant medical professional identity during orientation. Participants noted tensions during this period, many of which revolved around the dominant identity and their past, present and future selves. Longstanding issues of diversity and inclusion in medicine manifest from day one of medical school. While orientation may be intended as a transition period to welcome students into the profession, it is a crucial period for medical schools to intentionally establish a commitment to an inclusive culture. Waiting to do so after identity formation has already begun is a missed opportunity.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".