Video-based interventions to improve self-assessment accuracy among physicians: A systematic review
Bibliographic record
Abstract
PURPOSE: Self-assessment of a physician's performance in both procedure and non-procedural activities can be used to identify their deficiencies to allow for appropriate corrective measures. Physicians are inaccurate in their self-assessments, which may compromise opportunities for self- development. To improve this accuracy, video-based interventions of physicians watching their own performance, an experts' performance or both, have been proposed to inform their self-assessment. We conducted a systematic review of the effectiveness of video-based interventions targeting improved self-assessment accuracy among physicians. MATERIALS AND METHODS: The authors performed a systematic search of MEDLINE, Embase, EBM reviews, and Scopus databases from inception to August 23, 2022, using combinations of terms for "self-assessment", "video-recording", and "physician". Eligible studies were empirical investigations assessing the effect of video-based interventions on physicians' self-assessment accuracy with a comparison of self-assessment accuracy pre- and post- video intervention. We defined self-assessment accuracy as a "direct comparison between an external evaluator and self-assessment that was quantified using formal statistical analysis". Two reviewers independently screened records, extracted data, assessed risk of bias, and evaluated quality of evidence. A narrative synthesis was conducted, as variable outcomes precluded a meta-analysis. RESULTS: A total of 2,376 papers were initially retrieved. Of these, 22 papers were selected for full-text review; a final 9 studies met inclusion criteria for data extraction. Across studies, 240 participants from 5 specialties were represented. Video-based interventions included self-video review (8/9), benchmark video review (3/9), and/or a combination of both types (1/9). Five out of nine studies reported that participants had inaccurate self-assessment at baseline. After the intervention, 5 of 9 studies found a statistically significant improvement in self-assessment accuracy. CONCLUSIONS: Overall, current data suggests video-based interventions can improve self-assessment accuracy. Benchmark video review may enable physicians to improve self-assessment accuracy, especially for those with limited experience performing a particular clinical skill. In contrast, self-video review may be able to provide improvement in self-assessment accuracy for more experience physicians. Future research should use standardized methods of comparison for self-assessment accuracy, such as the Bland-Altman analysis, to facilitate meta-analytic summation.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".