Application of the R2C2 Model to In-the-Moment Feedback and Coaching
Bibliographic record
Abstract
PURPOSE: The R2C2 (relationship, reaction, content, coaching) model is an iterative, evidence-based, theory-informed approach to feedback and coaching that enables preceptors and learners to build relationships, explore reactions and reflections, confirm content, and coach for change and cocreate an action plan. This study explored application of the R2C2 model for in-the-moment feedback conversations between preceptors and learners and the factors that influence its use. METHOD: A qualitative study using framework analysis through the lens of experiential learning was undertaken with 15 trained preceptor-learner dyads. Data were collected during feedback sessions and follow-up interviews between March 2021 and July 2022. The research team familiarized themselves with the data, used a coding template to document examples of the model's application, reviewed the initial framework and revised the coding template, indexed and summarized the data, created a summary document, examined the transcripts for alignment with each model phase, and identified illustrative quotations and overarching themes. RESULTS: Fifteen dyads were recruited from 8 disciplines (11 preceptors were paired with a single resident [n = 9] or a single medical student [n = 2]; 2 preceptors each had 2 residents). All dyads were able to apply the R2C2 phases of building relationships, exploring reactions and reflections, and confirming content. Many struggled with the coaching components, specifically in creating an action plan and follow-up arrangements. Preceptor skill in applying the model, time available for feedback conversations, and the nature of the relationship impacted how the model was applied. CONCLUSIONS: The R2C2 model can be adapted to contexts where in-the-moment feedback conversations occur shortly after a clinical encounter. Experiential learning approaches applying the R2C2 model are critical. Skillful application of the model requires that learners and preceptors go beyond confirming an area of change and deliberately engage in coaching and cocreating an action plan.
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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 | 0.053 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".