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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".