In-the-Moment Feedback and Coaching: Improving R2C2 for a New Context
Bibliographic record
Abstract
BACKGROUND: The R2C2, a 4-phase feedback and coaching model, builds relationships, explores reactions, determines content and coaches for change, and facilitates formal feedback conversations between clinical supervisors/preceptors and residents. Formal discussions about performance are typically based on collated information from daily encounter sheets, objective structured clinical examinations, multisource feedback, and other data. This model has not been studied in settings where brief feedback and coaching conversations occur immediately after a specific clinical experience. OBJECTIVE: We explored how supervisors adapt the R2C2 model for in-the-moment feedback and coaching and developed a guide for its use in this context. METHODS: Eleven purposefully selected supervisors were interviewed in 2018 to explore where they used the R2C2 model, how they adapted it for in-the-moment conversations, and phrases used corresponding to each phase that could guide design of a new R2C2 in-the-moment model. RESULTS: Participants readily adapted the model to varied feedback situations; each of the 4 phases were relevant for conversations. Phase-specific phrases that could enable effective coaching conversations in a limited amount of time were identified. Data facilitated a revision of the original R2C2 model for in-the-moment feedback and coaching conversations and design of an accompanying trifold brochure to enable its effective use. CONCLUSIONS: The R2C2 in-the-moment model offers a systematic approach to feedback and coaching that builds on the original model, yet addresses time constraints and the need for an iterative conversation between the reaction and content phases. The model enables supervisors to coach and co-create an action plan with residents to improve performance.
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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 | 0.046 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".