Multisource Feedback in the Trauma Context: Priorities and Perspectives
Bibliographic record
Abstract
OBJECTIVES: Trauma resuscitations require competence in both clinical and nonclinical skills but these can be difficult to observe and assess. Multisource feedback (MSF) is workplace-based, involves the direct observation of learners, and can provide feedback on nonclinical skills. We sought to compare and contrast the priorities of multidisciplinary trauma team members when assessing resident trauma team captain (TTC) performance. Additionally, we aimed to describe the nature of the assessment and perceived the utility of incorporating MSF into the trauma context. METHODS: A convenience sample of 10 trauma team activations were observed. Following each activation, the attending physician trauma team leader (TTL), TTC, and a registered nurse (RN) participated in a semistructured interview. MSF was not provided to the TTC for the purpose of this study because MSF was not part of the assessment process of TTCs at the time of this study and maintaining anonymity may have encouraged more honest interview responses. Transcripts from each assessor group (TTL, TTC, RN) were coded and assigned to one of the five crisis resource management skills: leadership, communication, situational awareness, resource utilization, and problem-solving. Comments were also coded as positive, negative, or neutral as interpreted by the coder. RESULTS: All assessor groups mentioned communication skills most frequently. After communication, the RN and TTC groups commented on situational awareness most frequently, comprising 15 and 29% of their total responses, respectively, whereas 31% of the TTL comments focused on leadership skills. The RN and TTL groups provided positive assessments, with 51 and 42% of their respective comments coded as positive. Forty-five percent of self-assessment comments in the TTC group were negative. All (100%) of the TTC and TTL respondents felt that incorporating MSF would add to the quality of feedback, only 66% of the RN group felt that way. CONCLUSIONS: We found that each assessor group brings a unique focus and perspective to the assessment of resident TTC performance. The future inclusion of MSF in the trauma team context has the potential to enhance the learning environment in a clinical arena that is difficult to directly observe and assess.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".