A Randomized Trial of Instructor-Led Training Versus Video Lesson in Training Health Care Providers in Proper Donning and Doffing of Personal Protective Equipment
Bibliographic record
Abstract
OBJECTIVE: This study compared live instructor-led training with video-based instruction in personal protective equipment (PPE) donning and doffing. It assessed the difference in performance between (1) attending 1 instructor-led training session in donning and doffing PPE at 1 month prior to assessment, and (2) watching training videos for 1 month. METHODS: This randomized controlled trial pilot study divided 21 medical students and junior doctors into 2 groups. Control group participants attended 1 instructor-led training session. Video group participants watched training videos demonstrating the same procedures, which they could freely watch again at home. After 1 month, a doctor performed a blind evaluation of performance using checklists. RESULTS: Nineteen participants were assessed after 1 month. The mean donning score was 84.8/100 for the instructor-led group and 88/100 for the video group; mean effect size was 3.2 (95% CI: -7.5 to 9.5). The mean doffing score was 79.1/100 for the instructor-led group and 73.9/100 for the video group; mean effect size was 5.2 (95% CI: -7.6 to 18). CONCLUSION: Our study found no significant difference in donning and doffing scores between instructor-led and video lessons. Video training could be a fast and resource-efficient method of training in PPE donning and doffing in responding to the COVID-19 pandemic.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".