A Predeployment Trauma Team Training Course Creates Confidence in Teamwork and Clinical Skills: A Post-Afghanistan Deployment Validation Study of Canadian Forces Healthcare Personnel
Bibliographic record
Abstract
BACKGROUND: The 10-day Intensive Trauma Team Training Course (ITTTC) was developed by the Canadian Forces (CFs) to teach teamwork and clinical trauma skills to military healthcare personnel before deploying to Afghanistan. This article attempts to validate the impact of the ITTTC by surveying participants postdeployment. METHODS: A survey consisting of Likert-type multiple-choice questions was created and sent to all previous ITTTC participants. The survey asked respondents to rate their confidence in applying teamwork skills and clinical skills learned in the ITTTC. It explored the relevancy of objectives and participants' prior familiarity with the objectives. The impact of different training modalities was also surveyed. RESULTS: The survey showed that on average 84.29% of participants were "confident" or "very confident" in applying teamwork skills to their subsequent clinical experience and 52.10% were "confident" or "very confident" in applying clinical knowledge and skills. On average 43.74% of participants were "familiar" or "very familiar" with the clinical topics before the course, indicating the importance of training these skills. Participants found that clinical shadowing was significantly less valuable in training clinical skills than either animal laboratory experience or experience in human patient simulators; 68.57% respondents thought that ITTTC was "important" or "very important" in their training. CONCLUSIONS: The ITTTC created lasting self-reported confidence in CFs healthcare personnel surveyed upon return from Afghanistan. This validates the importance of the course for the training of CFs healthcare personnel and supports the value of team training in other areas of trauma and medicine.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".