Does the gender of the standardised patient influence candidate performance in an objective structured clinical examination?
Bibliographic record
Abstract
CONTEXT: The objective structured clinical examination (OSCE) requires the use of standardised patients (SPs). Recruitment of SPs can be challenging and factors assumed to be neutral may vary between SPs. On stations that are considered gender-neutral, either male or female SPs may be used. This may lead to an increase in measurement error. Prior studies on SP gender have often confounded gender with case. OBJECTIVE: The objective of this study was to assess whether a variation in SP gender on the same case resulted in a systematic difference in student scores. METHODS: At the University of Ottawa, 140 Year 3 medical students participated in a 10-station OSCE. Two physical examination stations were selected for study because they were perceived to be 'gender-neutral'. One station involved the physical examination of the back and the other of the lymphatic system. On each of the study stations, male and female SPs were randomly allocated. RESULTS: There was no difference in mean scores on the back examination station for students with female (6.96/10.00) versus male (7.04/10.00) SPs (P = 0.713). However, scores on the lymphatic system examination station showed a significant difference, favouring students with female (8.30/10.00) versus male (7.41/10.00) SPs (P < 0.001). Results were not dependent on student gender. CONCLUSIONS: The gender of the SP may significantly affect student performance in an undergraduate OSCE in a manner that appears to be unrelated to student gender. It would be prudent to use the same SP gender for the same case, even on seemingly gender-neutral stations.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".