USE OF SIMULATION-BASED SKILL ACQUISITION INSTRUCTION (SSAI) FOR MANUAL MASK VENTILATION (MMV) IMPROVES THE ABILITY OF NEONATAL RESUSCITATION PROGRAM (NRP) PROVIDERS TO DELIVER AND RETAIN EFFECTIVE MMV SKILLS
Bibliographic record
Abstract
Abstract BACKGROUND Ten percent of infants require resuscitation at birth. Effective manual mask ventilation (MMV) establishes functional residual capacity and delivers tidal volumes that promote gas exchange. A standard 4 to 6-hour NRP workshop does not ensure MMV competence. In fact, studies show that MMV is ineffective in 30 to 70% of simulated resuscitations. Effective MMV requires continual evaluation, and appropriate use of corrective steps when necessary. We hypothesize that simulation-based learning with video feedback improves MMV performance compared to traditional teaching. OBJECTIVES 1. To assess MMV skills in NRP providers before and after simulation-based skill acquisition instruction (SSAI) using video-feedback. 2. To compare MMV performance between SSAI intervention and control groups. 3. To determine retention of MMV skills at 3 to 4 months post-intervention. DESIGN/METHODS In this randomized controlled study, perinatal care providers who had completed a standard NRP workshop were randomly assigned to an intervention (SSAI) or control group. All participants were assessed and videorecorded performing a standard MMV skill station using a NeoNatalie™ manikin. The intervention group reviewed their videorecordings and received instructor feedback on MMV performance. The control group reviewed the MMV chapter in the NRP textbook and received instructor feedback using a standard performance checklist without reviewing their videorecording. Feedback included observation of chest rise. Participants were assessed at 3 periods: (a) after first MMV station, (b) after immediate feedback, and (c) 3 to 4 months later. Video recordings were scored by two investigators blinded to the randomization groups using a 13-item MMV skill scale. (0=not done; 1= done partly; 2 = done correctly) giving a maximum score of 26. We compared scores between the two groups. RESULTS Of the 91 participants, 46 were assigned to intervention and 45 to control group. The mean scores for the control group were: 20.1 (pre); 22.6 (post); and 21.6 (at 3–4 months post) compared to 22.1 (pre); 25.1 (post) and 24.7 (at 3–4 months post) for the intervention group. Repeated measures analysis showed that there is an effect of group and time, with the intervention group performing and retaining skills better at follow up, Wilks’ lambda = 0.66, F(3,67) = 11.31, p < .001. CONCLUSION Use of SSAI with video-feedback in the teaching of MMV skills improves the ability of NRP providers to deliver and retain effective MMV in a manikin. NRP providers should be trained using SSAI. Video-feedback should be considered during NRP training. Video-feedback following real-life resuscitations should be studied for its potential benefit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".