Trauma team leaders in Canada: A national survey
Bibliographic record
Abstract
Introduction The availability, composition and activation criteria for trauma teams vary across different health care systems, but little is known about these features in the Canadian health system. The aim of this study is to provide a description of the current trauma team available in Level 1 and 2 centres across Canada. Methods In 2017, using a modified Dillman technique, a survey was sent to 210 health professionals across all Canadian trauma care facilities, including questions that focused on (1) the presence and the composition of a trauma team, (2) the established criteria to activate this team and (3) the initial patient care. Results Overall, 107 (57%) completed surveys were received. Only 22 (11.7%) were from Level 1 or 2 centre and considered for compilation. Seventeen respondents have a trauma team in their centre, and they all shared their criteria for activating their team (1–27 different indications). The suspected injuries, the judgment of the emergency physician, the systolic blood pressure, the Glasgow Coma Score and the respiratory rate were the most frequently mentioned items. In the presence of a pre-hospital care warning, the initial assessment of a severely injured patient is exclusively completed by a member of the trauma team for only 35.1% of the respondents. For 11.8% of respondents, trauma team coordinates airway management. For 64.7% of participants, the trauma team leader is the dedicated care provider to accompany patients until the final destination. Conclusions The results suggest a great variability across Canada, regarding the roles assumed by the trauma team but also regarding the activation criteria leading them to take action.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".