A Foucauldian reading of the multiple mini-interview tool used in Canadian physiotherapy admissions
Bibliographic record
Abstract
Physiotherapy education in Canada, like many professional training programs, relies on categorization tools to determine who is most suitable for entry. One such tool is the Multiple Mini-Interview (MMI), an examination which is widely accepted as a fair, objective, and evidence-based method for assessing non-academic competencies. In this professional theoretical paper, we interrogate assumptions supporting its use through a Foucauldian reading of the MMI, examining it as an instantiation of disciplinary power. Rather than assessing whether the MMI "works" in predictive terms, we focus on the form of power underpinning yet concealed within it. By reading the MMI through a Foucauldian lens, we unsettle its taken-for-granted neutrality and call attention to the institutional logics that shape who gains access to the Canadian physiotherapy academy.We argue that, consistent with Foucault's theorization, the MMI encourages applicants to internalize and perform institutionally sanctioned norms of the "ideal" candidate, shaping admissions in ways that favor those with particular cultural and communicative skills who can anticipate and align with these expectations. These candidates are more likely to be perceived as "trainable" and therefore admissible. Thus, while framed as an equitable assessment, the MMI may reinforce Eurocentric and White institutional normativity, a recognized problem within Canadian physiotherapy programs. In this way, the MMI risks contributing to the profession's demographic homogeneity and perpetuating structural disadvantage for some applicants.
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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.020 | 0.056 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".