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Record W3004040745 · doi:10.3389/fped.2020.00014

Simulation-Based Summative Assessment of Neonatal Resuscitation Providers Using the RETAIN Serious Board Game—A Pilot Study

2020· article· en· W3004040745 on OpenAlexaffabout
Simran K. Ghoman, Maria Cutumisu, Georg M. Schmölzer

Bibliographic record

VenueFrontiers in Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsNeonatal resuscitationMedicineResuscitationTest (biology)Competence (human resources)Interquartile rangeSummative assessmentHealth careMedical emergencyEmergency medicineSurgeryFormative assessment

Abstract

fetched live from OpenAlex

Background Each year, 13-26 million newborn babies require help to breathe at birth. Healthcare professionals (HCPs) who provide neonatal resuscitative care must be frequently evaluated to maintain and improve the quality of healthcare delivered. While simulation-based competence assessment is preferred, resource constraints hinder uptake. We aimed to examine if the RETAIN simulation-based boardgame can be used to assess HCPs’ neonatal resuscitation knowledge. Method Twenty neonatal HCPs (19 females) from the Royal Alexandra Hospital (Edmonton, Canada) were recruited. First, they completed an open-answer written test of one neonatal resuscitation scenario. Then, they completed one neonatal resuscitation scenario of difficulty comparable to that of the open-answer written test, but this time using the RETAIN board game. In the RETAIN board game (https://playretain.com, RETAIN Labs Medical Inc, Edmonton, Canada), players perform simulated neonatal resuscitation scenarios based on real-life cases, using action cards and equipment pieces. Sessions were video-recorded and scored using Neonatal Resuscitation Program 2015 guidelines. Data are reported as mean (standard deviation) for normally distributed continuous variables, and as median (interquartile range) for non-normal continuous variables. Results Participants consisted of the following HCPs: 8 nurses, 4 respiratory therapists, 4 nurse practitioners, and 4 neonatal fellows with median(IQR) 10.5(3-17) years of clinical experience. Overall mean (SD) Open-answer test and Game Performance was 8.6(2.1) out of 16 possible points (53%) and 29(3.2) out of 40 possible points (74%), respectively. Out of the 10 actions shared between the open-answer test and game scenario, performance on the open-answer test was mean(SD) 7.2(1.3) (72%) and game performance was mean(SD) 8.8(1.4) (88%) (V=17, p<0.01). Conclusion RETAIN may provide an enjoyable and standardized alternative towards summative assessment of neonatal resuscitation providers. RETAIN may be used to improve more frequent and ubiquitous uptake of simulation-based competence assessment in healthcare settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.373
Teacher spread0.311 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations17
Published2020
Admission routes2
Has abstractyes

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