Remote assessment via video evaluation (RAVVE): a pilot study to trial video-enabled peer feedback on clinical performance
Bibliographic record
Abstract
BACKGROUND: Video review processes for evaluation and coaching are often incorporated into medical education as a means to accurately capture physician-patient interactions. Compared to direct observation they offer the advantage of overcoming many logistical challenges. However, the suitability and viability of using video-based peer consultations for professional development requires further investigation. This study aims to explore the acceptability and feasibility of video-based peer feedback to support professional development and quality improvement in patient care. METHODS: Five rheumatologists each provided four videos of patient consultations. Peers evaluated the videos using five-point scales, providing annotations in the video recordings, and offering recommendations. The rheumatologists reviewed the videos of their own four patient interactions along with the feedback. They were asked to document if they would make practice changes based on the feedback. Focus groups were conducted and analysed to explore the effectiveness of video-based peer feedback in assisting physicians to improve clinical practice. RESULTS: Participants felt the video-based feedback provided accurate and detailed information in a more convenient, less intrusive manner than direct observation. Observations made through video review enabled participants to evaluate more detailed information than a chart review alone. Participants believed that reviewing recorded consultations allowed them to reflect on their practice and gain insight into alternative communication methods. CONCLUSIONS: Video-based peer feedback and self-review of clinical performance is an acceptable and pragmatic approach to support professional development and improve clinical care among peer clinicians. Further investigation into the effectiveness of this approach is needed.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".