Seeking inclusion in an exclusive process: discourses of medical school student selection
Bibliographic record
Abstract
CONTEXT: Calls to increase medical class representativeness to better reflect the diversity of society represent a growing international trend. There is an inherent tension between these calls and competitive student selection processes driven by academic achievement. How is this tension manifested? METHODS: Our three-phase interdisciplinary research programme focused on the discourses of excellence, equity and diversity in the medical school selection process, as conveyed by key stakeholders: (i) institutions and regulatory bodies (the websites of 17 medical schools and 15 policy documents from national regulatory bodies); (ii) admissions committee members (ACMs) (according to semi-structured interviews [n = 9]), and (iii) successful applicants (according to semi-structured interviews [n = 14]). The work is theoretically situated within the works of Foucault, Bourdieu and Bakhtin. The conceptual framework is supplemented by critical hermeneutics and the performance theories of Goffman. RESULTS: Academic excellence discourses consistently predominate over discourses calling for greater representativeness in medical classes. Policy addressing demographic representativeness in medicine may unwittingly contribute to the reproduction of historical patterns of exclusion of under-represented groups. In ACM selection practices, another discursive tension is exposed as the inherent privilege in the process is marked, challenging the ideal of medicine as a meritocracy. Applicants' representations of self in the 'performance' of interviewing demonstrate implicit recognition of the power inherent in the act of selection and are manifested in the use of explicit strategies to 'fit in'. CONCLUSIONS: How can this critical discourse analysis inform improved inclusiveness in student selection? Policymakers addressing diversity and equity issues in medical school admissions should explicitly recognise the power dynamics at play between the profession and marginalised groups. For greater inclusion and to avoid one authoritative definition of excellence, we suggest a transformative model of faculty development aimed at promoting multiple kinds of excellence. Through this multi-pronged approach, we call for the profession to courageously confront the cherished notion of the medical meritocracy in order to avoid unwanted aspects of elitism.
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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 | 0.038 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.025 | 0.068 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| 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".