The role of video-assisted feedback sessions in resident teaching: A pre-post intervention
Bibliographic record
Abstract
Purpose: Despite providing a large component of teaching to trainees, internal medicine residents receive little feedback on their teaching ability. Methods: This was a single-center, mixed methods study of 19 senior internal medicine residents in Canada. Classroom-based teaching sessions delivered by the participants were individually video recorded. The individual recording was then watched by the participant and by two feedback facilitators, who then met for face-to-face feedback. Participants completed a self-reflective exercise after this intervention. Audience members of the recorded session and a post-feedback teaching session completed an evaluation form. Scores from the evaluation forms from each phase were analyzed with the Wilcoxon Signed-Rank Test. Inductive coding was performed for qualitative data from the feedback sessions and reflective exercises. Results: 19 residents participated. There was no statistical difference in the evaluation form scores between the pre-intervention and post-intervention teaching sessions. Mean scores varied from 4.6 to 5.0 out of 5.0 on combined pre-and post-intervention evaluations. 89% of participants found viewing their recorded session useful. 94% of residents stated the intervention was worth continuing. Common themes of feedback and self-evaluation included "time-management," "organization," "communication," and "environment." Conclusion: Video-assisted feedback of teaching improved self-perception of a resident's teaching ability.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.005 | 0.000 |
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".