Evidence-Informed Facilitated Feedback: The R2C2 Feedback Model
Bibliographic record
Abstract
Abstract Introduction While feedback continues to pose challenges, new understanding is emerging. Feedback is now being seen as an interaction in which learner engagement, supportive relationships, reflection, and cooperative planning are important. In response and through research, we developed and tested the R2C2 model and teaching materials to support its use. Methods R2C2 is an evidence-based reflective model for providing assessment feedback. It includes four phases: (1) relationship building, (2) exploring reactions to the feedback, (3) exploring understanding of feedback content, and (4) coaching for performance change. It provides a strategy for facilitating feedback conversations that promote engagement with performance data and enable coaching for improvement. This package of educational materials includes paper-based and video resources designed to support interactive learning and skills development in facilitating feedback and coaching. Specific strategies are described and demonstrated for each phase of the R2C2 model and include a learning change template for the coaching phase. Resources can be used by an individual or group. A workshop outline with presentation slides and a practice scenario are also included. Results Through research, invited and peer-reviewed presentations, and feedback from colleagues who have used the materials and the R2C2 model, we have learned that the model is intuitive and easy to use, that it can engage the learner and support coaching, and that the educational materials are clear and useful. Discussion The model is intuitive, especially within competency-based education, is easy to follow, and makes sense to faculty, which makes it easy to implement in most programs.
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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 | 0.125 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".