The one minute mentor: a pilot study assessing medical students' and residents' professional behaviours through recordings of clinical preceptors' immediate feedback.
Bibliographic record
Abstract
INTRODUCTION: The assessment of professional development and behaviour is an important issue in the training of medical students and physicians. Several methods have been developed for doing so. What is still needed is a method that combines assessment of actual behaviour in the workplace with timely feedback to learners. GOAL: We describe the development, piloting and evaluation of a method for assessing professional behaviour using digital audio recordings of clinical supervisors' brief feedback. We evaluate the inter-rater reliability, acceptability and feasibility of this approach. METHODS: Six medical students in Year 5 and three GP registrars (residents) took part in this pilot project. Each had a personal digital assistant (PDA) and approached their clinical supervisors to give approximately one minute of verbal feedback on professionalism-related behaviours they had observed in the registrar's clinical encounters. The comments, both in transcribed text format and audio, were scored by five evaluators for competence (the learner's performance) and confidence (how confident the evaluator was that the comment clearly described an observed behaviour or attribute that was relevant). Students and evaluators were surveyed for feedback on the process. RESULTS: Study evaluators rated 29 comments from supervisors in text and audio format. There was good inter-rater reliability (Cronbach alpha around 0.8) on competence scores. There was good agreement (paired t-test) between scores across supervisors for assessments of comments in both written and audio formats. Students found the method helpful in providing feedback on professionalism. Evaluators liked having a relatively objective approach for judging behaviours and attributes but found scoring audio comments to be time-consuming. DISCUSSION: This method of assessing learners' professional behaviour shows potential for providing both formative and summative assessment in a way that is feasible and acceptable to students and evaluators. Initial data shows good reliability but to be valid, training of clinical supervisors is necessary to help them provide useful comments based on defined behaviours and attributes of students. In addition, the validity of the scoring method remains to be confirmed.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".