A web‐based peer feedback tool for physical examination
Bibliographic record
Abstract
BACKGROUND: Medical students do not have many formal opportunities to practise physical examinations during their pre-clerkship years. Consequently, they often practise their examination skills with peers outside of formal teaching sessions. There are also few opportunities for observation and feedback on their skills in this area. CONTEXT: The undergraduate medical programme at the University of Toronto is a 4-year programme where students learn clinical skills in the first 2 years prior to beginning clinical rotations. INNOVATION: We describe a web-based, mobile device-friendly tool to facilitate structured peer-peer observation and feedback of physical examination skills. The tool is designed for use by pre-clerkship medical students, and includes assessment criteria for select physical examinations based on expectations for pre-clerkship medical students. In addition, supplemental instructional material was developed to aid the students' learning. The tool was piloted with first-year medical students as they prepared for their autumn objective structured clinical examination (OSCE) at the University of Toronto. Its use was voluntary. IMPLICATIONS: The tool has been used enthusiastically by students, and their feedback has been positive. This tool is an innovation that guides students as they practise their physical examination skills, and gives them a framework to provide feedback to one another during this process. It also encourages students to reflect critically on their own skills, as well as those of their peers, through the use of an engaging digital platform. The tool will be expanded to include history-taking vignettes, photos and videos. The tool is sustainable, and could be easily implemented at other institutions without a substantial investment. Students often practise their examination skills with peers outside of formal teaching sessions.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".