Self-assessment differences between genders in a low-stakes objective structured clinical examination (OSCE)
Bibliographic record
Abstract
OBJECTIVE: Physicians and medical students are generally poor-self assessors. Research suggests that this inaccuracy in self-assessment differs by gender among medical students whereby females underestimate their performance compared to their male counterparts. However, whether this gender difference in self-assessment is observable in low-stakes scenarios remains unclear. Our study's objective was to determine whether self-assessment differed between male and female medical students when compared to peer-assessment in a low-stakes objective structured clinical examination. RESULTS: Thirty-three (15 males, 18 females) third-year students participated in a 5-station mock objective structured clinical examination. Trained fourth-year student examiners scored their performance on a 6-point Likert-type global rating scale. Examinees also scored themselves using the same scale. To examine gender differences in medical students' self-assessment abilities, mean self-assessment global rating scores were compared with peer-assessment global rating scores using an independent samples t test. Overall, female students' self-assessment scores were significantly lower compared to peer-assessment (p < 0.001), whereas no significant difference was found between self- and peer-assessment scores for male examinees (p = 0.228). This study provides further evidence that underestimation in self-assessment among females is observable even in a low-stakes formative objective structured clinical examination facilitated by fellow medical 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 | 0.029 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".