Training the assessors for the General Medical Council’s Performance Procedures
Bibliographic record
Abstract
From July 1997, the General Medical Council (GMC) has had the power to investigate doctors whose performance is considered to be seriously deficient. Assessment procedures have been developed for all medical specialties to include peer review of performance in practice and tests of competence. Peer review is conducted by teams of at least two medical assessors and one lay assessor. A comprehensive training programme for assessors has been developed that simulates the context of a typical practice-based assessment and has been tailored for 12 medical specialties. The training includes the principles of assessment, familiarization with the assessment instruments and supervised practice in assessment methods used during the peer review visit. High fidelity is achieved through the use of actors who simulate third party interviewees and trained doctors who role play the assessee. A subgroup of assessors, selected to lead the assessment teams, undergo training in handling group dynamics, report writing and in defending the assessment report against legal challenge. Debriefing of assessors following real assessments has been strongly positive with regard to their preparedness and confidence in undertaking the assessment.
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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 | 0.103 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".