The Need for an Executive Leadership Curriculum in Scientist-Clinician Training Programs
Bibliographic record
Abstract
The health of Canadians depends on effective leadership among health care providers to facilitate the translation of new health discoveries into clinical practice. Clinician-scientists play an important role in bridging the gap between research and clinical practice, and require effective leadership skills to advance clinical practice successfully. To accelerate the leadership development in clinician scientist trainees, with the aim of developing strong leaders in administration and health advocacy, the Leaders in Medicine (LIM) training program at the University of Calgary created an Executive Leadership Coaching Program involving three phases: 1) an evidence-based evaluation tool, the Core Values IndexTM (CVI), that was used to identify the key drivers behind how individuals can be most effective in making their contribution; 2) small group workshops to debrief the results of the CVI assessment; and 3) one-on-one executive coaching sessions to facilitate the discovery, development and deployment of individual leadership capabilities. Coaching in leadership strategies enables clinician-scientist trainees to lead, influence, manage and deliver science-based improvements into the practice of medicine. We strongly recommend that other Canadian scientist-clinician training programs consider opportunities like the ones we offer to our LIM trainees. This training has important implications for the delivery of healthcare in Canada.
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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 | 0.060 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".