Comparing performance among male and female candidates in sex-specific clinical knowledge in the MRCGP
Bibliographic record
Abstract
BACKGROUND: Patients often seek doctors of the same sex, particularly for sex-specific complaints and also because of a perception that doctors have greater knowledge of complaints relating to their own sex. Few studies have investigated differences in knowledge by sex of candidate on sex-specific questions in medical examinations. AIM: The aim was to compare the performance of males and females in sex-specific questions in a 200-item computer-based applied knowledge test for licensing UK GPs. DESIGN AND SETTING: A cross-sectional design using routinely collected performance and demographic data from the first three versions of the Applied Knowledge Test, MRCGP, UK. METHOD: Questions were classified as female specific, male specific, or sex neutral. The performance of males and females was analysed using multiple analysis of covariance after adjusting for sex-neutral score and demographic confounders. RESULTS: Data were included from 3627 candidates. After adjusting for sex-neutral score, age, time since qualification, year of speciality training, ethnicity, and country of primary medical qualification, there were differences in performance in sex-specific questions. Males performed worse than females on female-specific questions (-4.2%, 95% confidence interval [CI] = -5.7 to -2.6) but did not perform significantly better than females on male-specific questions (0.3%, 95% CI = -2.6 to 3.2%. CONCLUSION: There was evidence of better performance by females in female-specific questions but this was small relative to the size of the test. Differential performance of males and females in sex-specific questions in a licensing examination may have implications for vocational and post-qualification general practice training.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".