R2C2 in Action: Testing an Evidence-Based Model to Facilitate Feedback and Coaching in Residency
Bibliographic record
Abstract
BACKGROUND: Feedback is increasingly seen as a collaborative conversation between supervisors and learners, where learners are actively and reflectively engaged with feedback and use it to improve. Based on this, and through earlier research, we developed an evidence- and theory-informed, 4-phase model for facilitating feedback and practice improvement-the R2C2 model (relationship, reaction, content, coaching). OBJECTIVE: Our goal was to explore the utility and acceptability of the R2C2 model in residency education, specifically for engaging residents in their feedback and in using it to improve, as well as the factors influencing its use. METHODS: This qualitative study used the principles of design research. We recruited residents and their supervisors in 2 programs, internal medicine and pediatrics. We prepared supervisors to use the R2C2 model during their regular midrotation and/or end-of-rotation feedback sessions with participating residents to discuss their progress and assessment reports. We conducted debriefing interviews with supervisors and residents after each session. We analyzed transcripts as a team using template and content analysis. RESULTS: Of 61 residents, 7 residents (11%) participated with their supervisors (n = 5). Schedules and sensitivity to feedback prevented broader enrollment. Supervisors found the structured R2C2 format useful. Residents and supervisors reported that the coaching phase was novel and helpful, and that the R2C2 model engaged both groups in collaborative, reflective, goal-oriented feedback discussions. CONCLUSIONS: Participants found that using the R2C2 model enabled meaningful feedback conversations, identification of goals for improvement, and development of strategies to meet those goals.
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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 | 0.114 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".