Comparing the impact of video feedback and group discussion on patient education performance among medical interns: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Effective patient education is critical in managing chronic diseases, improving patient outcomes, and reducing healthcare burdens. This study aimed to compare the impact of video feedback (VFB) and group discussion (GD) training on the patient education performance of medical interns. METHOD: In this double-blind randomized controlled trial, 78 medical interns at Shiraz University of Medical Sciences (2019) were assigned to VFB or GD groups using closed-envelope randomization. Both groups attended six 2-hour workshops with standardized scenarios. Performance was assessed using the Objective Structured Long Examination Record (OSLER) checklist before (OSLER1) and one month after the intervention (OSLER2). Primary outcome was the difference in mean post-intervention OSLER scores. Analyses included Mann-Whitney U and Wilcoxon tests. RESULT: Seventy-six interns completed the study. No significant differences in demographic features (p > 0.05) or baseline scores (p = 0.21). Both groups demonstrated significant improvements in OSLER scores after the intervention (P < 0.001). The GD group's mean OSLER1 score increased from 4.38 ± 1.04 to 8.43 ± 1.85 (p < 0.001), while the VFB group's scores rose from 5.10 ± 2.09 to 17.44 ± 1.10 (p < 0.001). The VFB group significantly outperformed the GD group in post-intervention scores (p < 0.001), with a mean score improvement of 12.33 ± 2.53 compared to 4.05 ± 2.05 in the GD group (p < 0.001). CONCLUSION: Both training methods improved interns' patient education performance, with VFB showing substantially greater gains. Video feedback may be a more effective approach for enhancing communication skills in clinical training. Further research is warranted to assess long-term impact and broader applicability. TRIAL REGISTRATION: A clinical trial number is not applicable.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".