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Record W2803476828 · doi:10.1093/pch/pxy054.123

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

2018· article· en· W2803476828 on OpenAlexaff
Harish Amin, Nalini Singhal, Tanya Beran, Majeeda Kamaluddeen, Parthiv Amin, Amuchou Soraisham, Paul H. Brown, Khalid Aziz

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChecklistCardiopulmonary resuscitationRandomized controlled trialCompetence (human resources)Neonatal resuscitationRandomizationComputer scienceDreyfus model of skill acquisitionVideo recordingMedical physicsResuscitationMedicinePsychologyMultimediaEmergency medicineSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.347
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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