MétaCan
Menu
Back to cohort
Record W4415351805 · doi:10.2196/69725

Correlation Between Electroencephalogram Brain-to-Brain Synchronization and Team Strategies and Tools to Enhance Performance and Patient Safety Scores During Online Hexad Virtual Simulation-Based Interprofessional Education: Cross-Sectional Correlational Study

2025· article· en· W4415351805 on OpenAlexvenueno aff
Atthaphon Viriyopase, Khuansiri Narajeenron

Bibliographic record

VenueJMIR Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCorrelationPatient safetySynchronization (alternating current)Metric (unit)Alpha (finance)Online assessment

Abstract

fetched live from OpenAlex

BACKGROUND: Team performance is crucial in crisis situations. Although the Thai version of Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) has been validated, challenges remain due to its subjective evaluation. To date, no studies have examined the relationship between electroencephalogram (EEG) activity and team performance, as assessed by TeamSTEPPS, during virtual simulation-based interprofessional education (SIMBIE), where face-to-face communication is absent. OBJECTIVE: This study aims to investigate the correlation between EEG-based brain-to-brain synchronization and TeamSTEPPS scores in multiprofessional teams participating in virtual SIMBIE sessions. METHODS: This single-center study involved 90 participants (15 groups of 6 simulated professionals: 1 medical doctor, 2 nurses, 1 pharmacist, 1 medical technologist, and 1 radiological technologist). Each group completed two 30-minute virtual SIMBIE sessions focusing on team training in a crisis situation involving COVID-19 pneumonia with a difficult airway, resulting in 30 sessions in total. The TeamSTEPPS scores of each participant across 5 domains were independently assessed by 2 trained raters based on screen recording, and their average values were used. The scores of participants in the same session were aggregated to generate a group TeamSTEPPS score, representing group-level performance. EEG data were recorded using wireless EEG acquisition devices and computed for total interdependence (TI), which represents brain-to-brain synchronization. The TI values of participants in the same session were aggregated to produce a group TI, representing group-level brain-to-brain synchronization. We investigated the Pearson correlations between the TI and the scores at both the group and individual levels. RESULTS: Interrater reliability for the TeamSTEPPS scores among 12 raters indicated good agreement on average (mean 0.73, SD 0.18; range 0.32-0.999). At the individual level, the Pearson correlations between the TI and the scores were weak and not statistically significant across all TeamSTEPPS domains (all adjusted P≥.05). However, strongly negative, statistically significant correlations between the group TI and the group TeamSTEPPS scores in the alpha frequency band (8-12 Hz) of the anterior brain area were found across all TeamSTEPPS domains after correcting for multiple comparisons (mean -0.87, SD 0.06; range -0.93 to -0.8). CONCLUSIONS: Strong negative correlations between the group TI and the group TeamSTEPPS scores were observed in the anterior alpha activity during online hexad virtual SIMBIE. These findings suggest that anterior alpha TI may serve as an objective metric for assessing TeamSTEPPS-based team performance.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.011
GPT teacher head0.398
Teacher spread0.387 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Medical EducationSame topicSimulation-Based Education in HealthcareFrench-language works237,207