Implementation of structured feedback in a psychiatry residency program in Canada: a qualitative analysis study
Bibliographic record
Abstract
Introduction: Structured feedback is important to support learner progression in competency-based medical education (CBME). R2C2 is an evidence-based four-phased feedback model that has been studied in a range of learner contexts; however, data on factors influencing implementation of this model are lacking. This pilot study describes implementation of the R2C2 model in a psychiatry CBME residency program, using the Consolidated Framework for Implementation Research (CFIR). Methods: = 10) supervisors' experience of the model. CFIR was used to identify factors that influence implementation of the R2C2 model when providing feedback to residents. Results: Qualitative data analysis revealed four key themes: Perceptions about the R2C2 model, Facilitators and barriers to its implementation, Fidelity to R2C2 model and Intersectionality related to the feedback. The CFIR implementation domains provided structure to the themes and subthemes. Conclusion: The R2C2 model is a helpful tool to provide structured feedback. Structure of the model, self-efficacy, in-house educational expertise, learning culture, organizational readiness, and training support are important facilitators of implementation. Further studies are needed to explore the learner's perspective and fidelity of this model.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".