Multi-source feedback following simulated resuscitation scenarios: a qualitative study
Bibliographic record
Abstract
Background: The direct observation and assessment of learners' resuscitation skills by an attending physician is challenging due to the unpredictable and time-sensitive nature of these events. Multisource feedback (MSF) may address this challenge and improve the quality of assessments provided to learners. We aimed to describe the similarities and differences in the assessment rationale of attending physicians, registered nurses, and resident peers in the context of a simulation-based resuscitation curriculum. Methods: We conducted a qualitative content analysis of narrative MSF of medical residents in their first postgraduate year of training who were participating in a simulation-based resuscitation course at two Canadian institutions. Assessments included an entrustment score and narrative comments from attending physicians, registered nurses, and resident peers in addition to self-assessment. Narrative comments were transcribed and analyzed thematically using a constant comparative method. Results: All 87 residents (100%) participating in the 2017-2018 course provided consent. A total of 223 assessments were included in our analysis. Four themes emerged from the narrative data: 1) Communication, 2) Leadership, 3) Demeanor, and 4) Medical Expert. Relative to other assessor groups, feedback from nurses focused on patient-centred care and communication while attending physicians focused on the medical expert theme. Peer feedback was the most positive. Self-assessments included comments within each of the four themes. Conclusions: In the context of a simulation-based resuscitation curriculum, MSF provided learners with different perspectives in their narrative assessment rationale and may offer a more holistic assessment of resuscitation skills within a competency-based medical education (CBME) program of assessment.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".