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Record W4399760915 · doi:10.1016/j.resplu.2024.100687

Teaching team competencies within resuscitation training: A systematic review

2024· review· en· W4399760915 on OpenAlexaff
Barbara Farquharson, Andrea Cortegiani, Kasper Glerup Lauridsen, Joyce Yeung, Robert Greif, Sabine Nabecker

Bibliographic record

VenueResuscitation Plus · 2024
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsSinai Health System
Fundersnot available
KeywordsMedical educationTraining (meteorology)ResuscitationPsychologyMedicineEmergency medicinePhysics

Abstract

fetched live from OpenAlex

Objectives: To evaluate the effectiveness of life support training with specific emphasis on team competencies on clinical and educational outcomes. Methods: This systematic review was prospectively registered (PROSPERO CRD42023473154) and followed the PICOST (population, intervention, comparison, outcome, study design, timeframe) format. All randomized controlled trials and non-randomized studies evaluating learners undertaking life support training with specific emphasis on team competencies in any setting (actual and simulated resuscitations) were included. Unpublished studies were excluded. Medline, Embase and Cochrane databases as well as trial registries were searched from inception to August 2023 (updated January 18, 2024). Two researchers performed title and abstract screening, full-text screening, data extraction, assessment of risk of bias (using RoB2 and ROBINS-I) and certainty of evidence (using GRADE). PRISMA reporting checklist was used to report the results. No funding was obtained to perform this systematic review. Results: The literature search identified 5470 manuscripts. After the removal of 2073 duplicates, reviewing the remaining articles' titles and abstracts yielded 31 articles for full-text review. Of these, 17 studies were finally included. The studies involved the following training levels: basic life support, adult advanced life support, paediatric and neonatal resuscitations. Most studies (n = 16) evaluated outcomes in simulated, and only one study in actual resuscitations. Studies included in all training contexts showed either neutrality and/or benefits of life support training with specific emphasis on team competencies. Team competencies training improved CPR skill performance and CPR quality. Specific team competencies that improved included leadership, communication, decision-making and task management. No undesirable effects were observed. Meta-analysis was not possible due to significant methodological heterogeneity. Sub-group analysis was impossible due to lack of data. Risk of bias assessment ranged from some concerns to serious. Overall certainty of evidence was rated as low to very low due to risk of bias and imprecision. Conclusion: This systematic review identified very low and low certainty evidence, almost entirely derived from simulation studies. The studies and their findings were heterogenous but suggest that teaching team competencies can improve resuscitation skills performance and CPR quality, as well as improve team competencies, specifically leadership, communication, decision-making, and task management. Further research is required to understand optimal configuration of team competencies training interventions and to understand the effect on clinical outcomes and cost-effectiveness.

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)0.018
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.065
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0130.010
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.143
GPT teacher head0.438
Teacher spread0.295 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations19
Published2024
Admission routes1
Has abstractyes

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