Comparing performance among male and female candidates in sex-specific clinical knowledge in the MRCGP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it