Competencies for Those Who Coach Physicians: A Modified Delphi Study
Bibliographic record
Abstract
The rapidly evolving coaching profession has permeated the health care industry and is gaining ground as a viable solution for addressing physician burnout, turnover, and leadership crises that plague the industry. Although various coach credentialing bodies are established, the profession has no standardized competencies for physician coaching as a specialty practice area, creating a market of aspiring coaches with varying degrees of expertise. To address this gap, we employed a modified Delphi approach to arrive at expert consensus on competencies necessary for coaching physicians and physician leaders. Informed by the National Board of Medical Examiners' practice of rapid blueprinting, a group of 11 expert physician coaches generated an initial list of key thematic areas and specific competencies within them. The competency document was then distributed for agreement rating and comment to over 100 stakeholders involved in physician coaching. Our consensus threshold was defined at 70% agreement, and actual responses ranged from 80.5% to 95.6% agreement. Comments were discussed and addressed by 3 members of the original group, resulting in a final model of 129 specific competencies in the following areas: (1) physician-specific coaching, (2) understanding physician and health care context, culture, and career span, (3) coaching theory and science, (4) diversity, equity, inclusion, and other social dynamics, (5) well-being and burnout, and (6) physician leadership. This consensus on physician coaching competencies represents a critical step toward establishing standards that inform coach education, training, and certification programs, as well as guide the selection of coaches and evaluation of coaching in health care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".