Coaching in Competence by Design: A New Model of Coaching in the Moment and Coaching Over Time to Support Large Scale Implementation
Bibliographic record
Abstract
Coaching is an increasingly popular means to provide individualized, learner-centered, developmental guidance to trainees in competency based medical education (CBME) curricula. Aligned with CBME’s core components, coaching can assist in leveraging the full potential of this educational approach. With its focus on growth and improvement, coaching helps trainees develop clinical acumen and self-regulated learning skills. Developing a shared mental model for coaching in the medical education context is crucial to facilitate integration and subsequent evaluation of success. This paper describes the Royal College of Physicians and Surgeons of Canada’s coaching model, one that is theory based, evidence informed, principle driven and iteratively and developed by a multidisciplinary team. The coaching model was specifically designed, fit for purpose to the postgraduate medical education (PGME) context and implemented as part of Competence by Design (CBD), a new competency based PGME program. This coaching model differentiates two coaching roles, which reflect different contexts in which postgraduate trainees learn and develop skills. Both roles are supported by the RX-OCR process: developing Relationship/Rapport, setting eXpectations, Observing, a Coaching conversation, and Recording/Reflecting. The CBD Coaching Model and its associated RX-OCR faculty development tool support the implementation of coaching in CBME. Coaching in the moment and coaching over time offer important mechanisms by which CBD brings value to trainees. For sustained change to occur and for learners and coaches to experience the model’s intended benefits, ongoing professional development efforts are needed. Early post implementation reflections and lessons learned are provided.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".