Catalytic effect of multisource feedback for trauma team captains: a mixed-methods prospective study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact and feasibility of multisource feedback compared with traditional feedback for trauma team captains (TTCs). DESIGN: A mixed-methods, non-randomised prospective study. SETTING: A level one trauma centre in Ontario, Canada. PARTICIPANTS: Postgraduate medical residents in emergency medicine and general surgery participating as TTCs. Selection was based on a convenience sampling method. INTERVENTION: Postgraduate medical residents participating as TTCs received either multisource feedback or standard feedback following trauma cases. MAIN OUTCOME MEASURES: TTCs completed questionnaires designed to measure the self-reported intention to change practice (catalytic effect), immediately following a trauma case and 3 weeks later. Secondary outcomes included measures of perceived benefit, acceptability, and feasibility from TTCs and other trauma team members. RESULTS: Data were collected following 24 trauma team activations: TTCs from 12 activations received multisource feedback and 12 received standard feedback. The self-reported intention for practice change was not significantly different between groups initially (4.0 vs 4.0, p=0.57) and at 3 weeks (4.0 vs 3.0, p=0.25). Multisource feedback was perceived to be helpful and superior to the existing feedback process. Feasibility was identified as a challenge. CONCLUSIONS: The self-reported intention for practice change was no different for TTCs who received multisource feedback and those who received standard feedback. Multisource feedback was favourably received by trauma team members, and TTCs perceived multisource feedback as useful for their development.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".