Multidisciplinary teams performing simulated trauma resuscitation have improved teamwork scores based on level of experience
Bibliographic record
Abstract
Background Teaching and assessment of non-technical skills in trauma and resuscitation continues to evolve. Simulation provides a safe environment where individuals can acquire these skills. Trauma and resuscitation are seldom an individual effort. For this reason teams could benefit from a shared model of instruction and learning. Teamwork skills are an important and natural starting point. We hypothesized that more experienced individuals should display superior teamworking skills. This should be true in the setting of individual or team assessment. Objectives We assessed multidisciplinary trauma teams with different levels of experience performing simulated trauma resuscitation using individual, team-based, and subjective teamwork assessment tools. Method Four teams completed two different simulated trauma resuscitation scenarios using a Human Patient Simulator. Each team had three team members, including a trauma team leader, an airway manager, and a trauma nurse. The teams varied by experience, including a student team, a junior resident and nurse team, a senior resident and nurse team and a staff physician and highly experienced nurse team. By retrospective video review, two independent raters scored the teams using two established teamwork assessment tools. The BARS (Behaviorally Anchored Rating Scale) was used to assess individual teamwork skills, and the TPOT (Team Performance Observation Tool) was used to assess global teamwork skills for each multidisciplinary team. Participants also completed a subjective teamwork questionnaire at the conclusion of the second scenario. Mean differences in scores for the BARS, TPOT and the subjective questionnaire were analyzed using ANOVA statistical analysis. Correlation studies of inter-rater reliability for the BARS and TPOT were performed using Pearson’s Coefficient. Results Mean BARS, TPOT and subjective teamwork questionnaire scores improved significantly with increasing levels of team experience (p
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".