Formative assessment of GP trainees’ clinical skills
Bibliographic record
Abstract
BACKGROUND: Clinical skill assessment (CSA) has been an integral part of the Royal College of General Practitioners' membership examination (MRCGP) since 2008. It is an expensive, high-stakes examination with first time pass rates ranging from 76.4 to 81.3. In this paper we describe the South East Scotland Deanery, NHS Education Scotland, pilot of a formative clinical skills assessment (fCSA) using the principles of formative assessment and OSCE. The purpose of the study was to assess the acceptability of the fCSA and to examine whether trainees, identified during the fCSA as 'at risk of failing the MRCGP CSA exam', are more likely to fail the MRCGP CSA exam later on in the year. METHODS: Trainees were assessed in four clinical skills stations under exam conditions. After each station they were given verbal feedback and subsequently both trainee and their trainer received written feedback. We assessed the value of the exercise through written feedback from trainees and trainers. Each trainee's performance in fCSA was triangulated with trainer assessment to identify 'flagged trainees'. We compared flagged and non-flagged trainees' performance in MRCGP CSA. RESULTS: Both trainees and trainers highly rated the fCSA. Overall 97% of non-flagged trainees have passed the RCGP CSA exam by May of that year in comparison to 80% of flagged trainees who have passed the RCGP CSA (P = 0.005). Trainers and trainees rated the fCSA as excellent and useful. CONCLUSION: We were able to demonstrate that the fCSA can be used to identify those trainees likely to fail the RCGP CSA. Contrary to reservations about the potential to demoralise trainees, the fCSA was viewed as a useful and a positive experience by both trainees and trainers. In addition, we suggest that feedback from fCSA was useful in triggering appropriate educational interventions. Early intervention with trainees who are predicted to fail the CSA has the potential to reduce deaneries overall fail rate. Preventing one trainee failure could save over £30 000.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".