The Effect of Gender Interactions on Studentsʼ Physical Examination Ratings in Objective Structured Clinical Examination Stations
Bibliographic record
Abstract
PURPOSE: Previous studies have reached a variety of conclusions regarding the effect of gender on performance in objective structured clinical examinations (OSCEs). Most measured the effect on students' overall OSCE score. The authors of this study evaluated the effect of gender on the scores of specific physical examination OSCE stations, both "gender-sensitive" and "gender-neutral." METHOD: In 2008, the authors collected scores for 138 second-year medical students at the University of Calgary who underwent a seven-station OSCE. Two stations--precordial and respiratory exams--were considered gender-sensitive. Multiple linear regression was used to explore the effect of students', standardized patients' (SPs'), and raters' genders on the students' scores. RESULTS: All 138 students (69 female) completed the OSCE and were included in the analyses. The mean scores (SD) for the two stations involving examination of the chest were higher for female than for male students (83.2% [15.5] versus 78.3% [15.8], respectively, d = 0.3, P = .009). There was a significant interaction between student and SP gender (P = .02). In the stratified analysis, female students were rated significantly higher than male students at stations with female SPs (85.4% [15.5] versus 76.6% [16.5], d = 0.6, P = .004) but not at stations with male SPs (80.2% [15.0] versus 80.0% [15.0], P = 1.0). CONCLUSION: These results suggest student and SP genders interact to affect OSCE scores at stations that require examination of the chest. Further investigations are warranted to ensure that the OSCE is an equal experience for all students.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".