Facilitated Reflective Performance Feedback
Bibliographic record
Abstract
PURPOSE: To develop and conduct feasibility testing of an evidence-based and theory-informed model for facilitating performance feedback for physicians so as to enhance their acceptance and use of the feedback. METHOD: To develop the feedback model (2011-2013), the authors drew on earlier research which highlights not only the factors that influence giving, receiving, accepting, and using feedback but also the theoretical perspectives which enable the understanding of these influences. The authors undertook an iterative, multistage, qualitative study guided by two recognized research frameworks: the UK Medical Research Council guidelines for studying complex interventions and realist evaluation. Using these frameworks, they conducted the research in four stages: (1) modeling, (2) facilitator preparation, (3) model feasibility testing, and (4) model refinement. They analyzed data, using content and thematic analysis, and used the findings from each stage to inform the subsequent stage. RESULTS: Findings support the facilitated feedback model, its four phases-build relationship, explore reactions, explore content, coach for performance change (R2C2)-and the theoretical perspectives informing them. The findings contribute to understanding elements that enhance recipients' engagement with, acceptance of, and productive use of feedback. Facilitators reported that the model made sense and the phases generally flowed logically. Recipients reported that the feedback process was helpful and that they appreciated the reflection stimulated by the model and the coaching. CONCLUSIONS: The theory- and evidence-based reflective R2C2 Facilitated Feedback Model appears stable and helpful for physicians in facilitating their reflection on and use of formal performance assessment feedback.
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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 | 0.038 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".