Discipline-specific competency-based curricula for leadership learning in medical specialty training
Bibliographic record
Abstract
Purpose Doctors play a central role in leading improvements to healthcare systems. Leadership knowledge and skills are not inherent, however, and need to be learned. General frameworks for medical leadership guide curriculum development in this area. Explicit discipline-linked competency sets and programmes provide context for learning and likely enhance specialty trainees' capability for leadership at all levels. The aim of this review was to summarise the scholarly literature available around medical specialty-specific competency-based curricula for leadership in the post-graduate training space. Design/methodology/approach A systematic literature search method was applied using the Medline, EMBASE and ERIC (education) online databases. Documents were reviewed for a complete match to the research question. Partial matches to the study topic were noted for comparison. Findings In this study, 39 articles were retrieved in full text for detailed examination, of which 32 did not comply with the full inclusion criteria. Seven articles defining discipline-linked competencies/curricula specific to medical leadership training were identified. These related to the areas of emergency medicine, general practice, maternal and child health, obstetrics and gynaecology, pathology, radiology and radiation oncology. Leadership interventions were critiqued in relation to key features of their design, development and content, with reference to modern leadership concepts. Practical implications There is limited discipline-specific guidance for the learning and teaching of leadership within medical specialty training programmes. The competency sets identified through this review may aid the development of learning interventions and tools for other medical disciplines. Originality/value The findings of this study provide a baseline for the further development, implementation and evaluation work required to embed leadership learning across all medical specialty training programmes.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".