P030: Multisource feedback for emergency medicine residents: different, relevant and useful information
Bibliographic record
Abstract
Introduction/Innovation Concept: Feedback provided to residents by physicians emphasize the medical expertise competency and may limit the attention paid to other CanMEDS competencies. Recent years have seen the emergence of the concept of multisource feedback, a process through which different members of the care team assess and provide feedback to residents. This approach is considered one of the best for providing relevant feedback on competencies that are less often addressed by physicians. To date, very few studies have explored emergency residents’ perceptions following feedback from their physicians, nurses with whom they have worked, and patients they have treated. Methods: In the emergency department of a tertiary-care university hospital, 10 emergency medicine residents participated, on a voluntary basis, in individual and semi-structured group interviews, three months after having received multisource feedback. Two researchers then qualitatively analyzed the data collected in those interviews. Thematic content analysis using QDA Miner identified dominant themes in the residents’ perceptions. Curriculum, Tool, or Material: Multisource feedback tool: Three questionnaires were designed to gather assessment from different sources: physicians, nurses, and patients. The questionnaires were adapted from those created by Joshi and colleagues for use in a study of residents’ competency in interpersonal and communication skills. During a nine months period, the residents distributed questionnaires to physicians, nurses, and patients with whom they felt they had enough interactions during their clinical shifts. Data from the questionnaires were compiled by two educators that prepared individual feedback reports for each resident. An educator was asked to conduct individual meetings with each resident to present the feedback report and discuss its content. Conclusion: Each source provided relevant comments that differed significantly in their content. Physicians focused primarily on medical expertise, whereas nurses addressed competencies related to management, collaboration, and communication, and patients commented on the competencies of professionalism and communication. Residents concluded that obtaining feedback from nurses and patients was not only acceptable but useful in their training. Several reported modifying certain behaviours after receiving the multisource feedback. Multisource feedback appears to have obvious teaching potential to provide feedback on competencies other than medical expertise in emergency residents.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".