How can I help at this moment? Outlining three generations of coaching for health professions educators
Bibliographic record
Abstract
BACKGROUND: Coaching is increasingly recognised as a valuable tool in health professions education (HPE), supporting learning, performance, and well-being. Yet, the term 'coaching' is used inconsistently, leading to confusion and limiting its potential impact. In this cross-cutting edge article, the authors draw upon a framework from the sport psychology and organisational development literatures to outline three distinct generations of coaching that can guide HPE away from treating coaching as a unitary construct. In doing so, we strive to clarify coaching's varied purposes and paradigms. THE THREE GENERATIONS FRAMEWORK: Generation 1 emphasises performance management, focusing on goal-setting and problem-solving. Generation 2 shifts towards personal development, leveraging strengths and fostering long-term growth. Generation 3 centers on meaning-making and cultural transformation, prioritising values, identity and reflective dialogue. The authors argue that each generation offers unique benefits and limitations and that coaching in HPE should be deliberately aligned with the learner's context and goals. Through examples from clinical education, they illustrate how different coaching approaches can support technical skill development, professional identity formation and well-being. IMPLICATIONS: Rather than advocating for a single model, the article encourages educators to adopt a flexible, context-sensitive approach to coaching. By understanding the generational distinctions, HPE professionals can better tailor coaching interventions to meet learners' evolving needs and foster sustainable development. As coaching continues to expand in scope and complexity, this framework offers a timely lens for enhancing clarity, intentionality, and impact in health professions education.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".