True transition to practice: a role-reversal simulation
Bibliographic record
Abstract
Implication Statement: This article explores a direct-observation simulation swapping resident and consultant roles as a measure to assess competence during the final "transition to practice" phase of residency. As indicated by the Royal College, assessment of competency in this stage should include direct observation; however, this is challenging to implement, both from the perspective of a busy clinical environment, but also logistically, as a final-stage resident is still a learner in a consultant clinic. Our suggested approach allows for both real-world experience for the resident as well as direct observation and assessment by the consultant, thus providing the resident with targeted, actionable feedback, as well as ensuring the final-stage resident is competent for practice. Énoncé des implications de la recherche: Cet article explore une simulation utilisant l'observation directe et où les rôles de résident et de consultant sont inversés comme moyen d'évaluation des compétences durant l'étape finale de la résidence, la « transition vers la pratique ». Le Collège royal indique qu'à ce stade, l'observation directe doit faire partie de l'évaluation des compétences; or, cette modalité d'évaluation est difficile à mettre en œuvre dans un environnement clinique animé et un contexte logistique où le résident est encore un apprenant dans une clinique de consultants. L'approche que nous proposons permet à la fois au résident d'acquérir une situation réelle et au consultant de faire une observation directe pour l'évaluation, et d'offrir une rétroaction ciblée et utile, tout en s'assurant que le résident en fin de parcours a les compétences requises pour pratiquer.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".