S.T.A.R.T.T.
Bibliographic record
Abstract
BACKGROUND: Most medical errors are nontechnical and include failures in team communication, situational awareness, resource use, and leadership. Other high-risk industries have adopted team-based crisis resource management (CRM) training strategies to address "nontechnical" skills and to improve human error and safety. Here, we describe the development and evaluation of a national multidisciplinary trauma CRM curriculum. METHODS: A needs analysis survey was distributed to general surgery program directors across Canada. With the use of this feedback, a course called STARTT [Standardized Trauma and Resuscitation Team Training] was developed and held in conjunction with the Canadian Surgery Forum. Participants completed a precourse and postcourse evaluation exploring changes in attitudes toward simulation and CRM principles using previously validated instruments. RESULTS: Twenty surgical residents, 6 nurses, 4 respiratory therapists, and 11 instructors (trauma surgeons, emergency physicians, nurses, and intensivists) participated. Of the participants, 100% completed the survey. Satisfaction was very high, with 97.5% of the participants rating the course as "good" or "excellent" and 97.5% recommending it to others. The presurvey and postsurvey showed statistically significant improvement in attitudes toward simulation and overall CRM principles (136.3 vs. 140.3 of 170, p = 0.004) following the course, primarily in the domain of teamwork (69.1 vs. 72.0 of 85, p = 0.002). CONCLUSION: Creation of a national multidisciplinary trauma CRM curriculum is feasible, has high satisfaction among participants, and can improve attitudes toward the importance of simulation and CRM principles with the ultimate goal of improving patient safety and care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.695 | 0.527 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".