Revalidation for general practitioners: randomised comparison of two revalidation models
Bibliographic record
Abstract
OBJECTIVE: To compare two models of revalidation for general practitioners. DESIGN: Randomised comparison of two revalidation models. SETTING: Primary care in Tayside, Scotland. PARTICIPANTS: 66 Tayside general practitioners (principals and non-principals), 53 of whom completed the revalidation folders. Interventions Two revalidation models: a minimum criterion based model with revalidation as the primary purpose, and an educational outcome model with emphasis on combining revalidation with continuing professional development. MAIN OUTCOME MEASURES: Feasibility and acceptability of each approach and effect on the doctor's continuing professional development. The ability to make a summative judgment on completed models and whether either model would allow patient groups to have confidence in the revalidation process. RESULTS: The criterion model was preferred by general practitioners. For both models doctors reported making changes to their practice and felt a positive effect on their continuing professional development. Summative assessment of the folders showed reasonable inter-rater reliability. CONCLUSIONS: The criterion model provides a practical and acceptable model for general practitioners to use when preparing for revalidation.
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 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 | 0.031 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".