Introducing recent medical graduates as members of Script Concordance Test expert reference panels: what impact?
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. The Script Concordance Test (SCT) is being increasingly used in professional development in clinical reasoning, with linear progression in performance in SCT's observed with increasing clinical experience. One of the limiting factors for the SCT is potential burnout in expert reference panel (ERP) members, which we have attempted to address by the introduction of recent medical graduates as panel members. We sought to evaluate the effect of introducing recent medical graduates in to our ERP's on pass/fail decisions in the final clinical reasoning examination of the 6-year undergraduate program of the University of Adelaide, Australia. We engaged an ERP comprising 50 faculty members from three collaborating universities and 13 recent medical graduates to answer on line an identical 20 case scenario, 50 question multidisciplinary SCT twice 6 months apart. The questions were used in high stakes end of year assessment of 5 th year medical students (n=132). The pass mark set by the experienced, specialist members of the panel was 49.6% and this increased to 50.4% by addition of recent medical graduates to the panel. This difference would have had no effect on fail rates estimated from the data from the cohort of 132 medical student candidates. In the context of assessment of clinical reasoning in medical programs, recent medical graduates are suitable members of SCT ERP's, and their contribution can enrich the panel and might help to minimise risk of burnout of more experienced faculty.
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 | 0.022 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".