Emergency skills learning on video (ESLOV): A single-blinded randomized control trial of teaching common emergency skills using self-instruction video (SIV) versus traditional face-to-face (FTF) methods
Bibliographic record
Abstract
BACKGROUND: Self-instruction video (SIV) has been widely explored as a teaching mode for cardiopulmonary resuscitation (CPR) and automated external defibrillation (AED), but not with other basic emergency skills. AIM: To evaluate the effectiveness of SIV in teaching other basic emergency skill in comparison with traditional face-to-face (FTF) methods. METHODS: Participants were randomized into SIV and FTF groups. Each group was assigned to learn basic airway management (BAM), cervical collar application (CCA), manual cardiac defibrillation (MCD), and emergency extremity splinting (EES) skills. Confidence level was assessed using questionnaires, and skills performances were assessed using calibrated-blinded assessors through an Objective Structured Clinical Examination (OSCE). RESULTS: Forty-five participants took part in the assessment exercises. There were no significant differences between both groups, on all four skill categories. The mean OSCE-score of an individual category between the FTF-group vs. the SIV-group were as follows: BAM (10.23 ± 1.04 vs. 10.04 ± 1.49; p = 0.62); CCA (7.86 ± 4.39 vs. 7.13 ± 4.12; p = 0.57); MCD (8.24 ± 0.89 vs. 7.58 ± 1.14; p = 0.39); EES (5.43 ± 2.11 vs. 4.63 ± 2.30; p = 0.23). The composite mean score for the FTF-group was 6.85, and for the SIV-group was 6.20 (p < 0.05). There was no significant different in the level of confidence for both groups. CONCLUSION: SIV is as effective as FTF in teaching and learning basic emergency skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".