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Record W2587399541 · doi:10.1097/pcc.0000000000001025

Improved Clinical Performance and Teamwork of Pediatric Interprofessional Resuscitation Teams With a Simulation-Based Educational Intervention*

2017· article· en· W2587399541 on OpenAlexafffundabout
Elaine Gilfoyle, Deanna Koot, John C. Annear, Farhan Bhanji, Adam Cheng, Jonathan P. Duff, Vincent Grant, Cecilia E. St. George-Hyslop, Nicole Delaloye, Afrothite Kotsakis, Carolyn McCoy, Christa Ramsay, Matthew J. Weiss, Ronald Gottesman

Bibliographic record

VenuePediatric Critical Care Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsSickKids FoundationHospital for Sick ChildrenChildren's Hospital of Eastern OntarioUniversity of CalgaryMcGill UniversityNew Brunswick Community CollegeUniversité LavalUniversity of AlbertaMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineTeamworkDebriefingPsychological interventionResuscitationIntensive carePhysical therapyEmergency medicineMedical emergencyNursingIntensive care medicineMedical education

Abstract

fetched live from OpenAlex

OBJECTIVES: To measure the effect of a 1-day team training course for pediatric interprofessional resuscitation team members on adherence to Pediatric Advanced Life Support guidelines, team efficiency, and teamwork in a simulated clinical environment. DESIGN: Multicenter prospective interventional study. SETTING: Four tertiary-care children's hospitals in Canada from June 2011 to January 2015. SUBJECTS: Interprofessional pediatric resuscitation teams including resident physicians, ICU nurse practitioners, registered nurses, and registered respiratory therapists (n = 300; 51 teams). INTERVENTIONS: A 1-day simulation-based team training course was delivered, involving an interactive lecture, group discussions, and four simulated resuscitation scenarios, each followed by a debriefing. The first scenario of the day (PRE) was conducted prior to any team training. The final scenario of the day (POST) was the same scenario, with a slightly modified patient history. All scenarios included standardized distractors designed to elicit and challenge specific teamwork behaviors. MEASUREMENTS AND MAIN RESULTS: Primary outcome measure was change (before and after training) in adherence to Pediatric Advanced Life Support guidelines, as measured by the Clinical Performance Tool. Secondary outcome measures were as follows: 1) change in times to initiation of chest compressions and defibrillation and 2) teamwork performance, as measured by the Clinical Teamwork Scale. Correlation between Clinical Performance Tool and Clinical Teamwork Scale scores was also analyzed. Teams significantly improved Clinical Performance Tool scores (67.3-79.6%; p < 0.0001), time to initiation of chest compressions (60.8-27.1 s; p < 0.0001), time to defibrillation (164.8-122.0 s; p < 0.0001), and Clinical Teamwork Scale scores (56.0-71.8%; p < 0.0001). A positive correlation was found between Clinical Performance Tool and Clinical Teamwork Scale (R = 0.281; p < 0.0001). CONCLUSIONS: Participation in a simulation-based team training educational intervention significantly improved surrogate measures of clinical performance, time to initiation of key clinical tasks, and teamwork during simulated pediatric resuscitation. A positive correlation between clinical and teamwork performance suggests that effective teamwork improves clinical performance of resuscitation teams.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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

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 head0.043
GPT teacher head0.457
Teacher spread0.414 · 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

Citations113
Published2017
Admission routes3
Has abstractyes

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