Developing and Implementing a Multisource Feedback Tool to Assess Competencies of Emergency Medicine Residents in the United States
Bibliographic record
Abstract
OBJECTIVES: Multisource feedback (MSF) has potential value in learner assessment, but has not been broadly implemented nor studied in emergency medicine (EM). This study aimed to adapt existing MSF instruments for emergency department implementation, measure feasibility, and collect initial validity evidence to support score interpretation for learner assessment. METHODS: Residents from eight U.S. EM residency programs completed a self-assessment and were assessed by eight physicians, eight nonphysician colleagues, and 25 patients using unique instruments. Instruments included a five-point rating scale to assess interpersonal and communication skills, professionalism, systems-based practice, practice-based learning and improvement, and patient care. MSF feasibility was measured by percentage of residents who collected the target number of instruments. To develop internal structure validity evidence, Cronbach's alpha was calculated as a measure of internal consistency. RESULTS: 2,100) with respective response rates of 67.2, 75.2, and 77.5%. Cronbach's alpha values for physicians, nonphysicians, patients, and self were 0.97, 0.97, 0.96, and 0.96, respectively. CONCLUSIONS: This study demonstrated that MSF implementation is feasible, although challenging. The tool and its scale demonstrated excellent internal consistency. EM educators may find the adaptation process and tools applicable to their learners.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".