The show must go on? Patients, props and pedagogy in the theatre of the <scp>OSCE</scp>
Bibliographic record
Abstract
According to Shakespeare, all the world's a stage, and all the men and women merely players. The objective structured clinical examination (OSCE), that most ubiquitous form of assessment in health professions education, offers us a particular instance of this maxim. Comprising at first glance a world of psychometric data, detailed checklists and global rating scales, the OSCE sets out to facilitate the assessment of a candidate's competence in a highly standardised and objective fashion. Despite this clear intention, OSCEs also offer a rich vein of (often unacknowledged) social and cultural processes. In this commentary, we draw on Goffman's dramaturgy metaphor and our experiences to undertake a wry examination of some of the least intended consequences of OSCEs. We take a satirical look at both the potential impact on patients and the pedagogical implications of this form of assessment. We now urge you to sit back, settle in and enjoy the show, as we raise the curtain on this one-night-only performance!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".