From Pilot Project to Annual Success: Creating an Evidence—Based Leadership Program for Medical Directors in Long—Term Care
Bibliographic record
Abstract
Engaging physicians in health care administration is critical. Within Canada, physician leadership programs have not been designed to meet the needs of medical directors in Long-Term Care (LTC). This article explains how a pilot program for medical directors in LTC was created to develop their leadership skills, and how it has now become an annual event. The program must evolve to enable medical directors to participate in system change and innovation within LTC. Il est essentiel de faire participer les médecins à l'administration de la santé. Au Canada, les programmes de leadership des médecins ne sont pas conçus pour répondre aux besoins des directeurs médicaux en soins de longue durée (SLD). Le présent article expose la création d'un projet pilote à l'intention de directeurs médicaux en SLD afin qu'ils développent leurs qualités de chef, ainsi que la transformation de ce projet en événement annuel. Le programme doit évoluer afin de permettre aux directeurs médicaux de participer aux modifications du système et à l'innovation en SLD.
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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 | 0.099 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".