Developing a Physician Management & Leadership Program (PMLP) in Newfoundland and Labrador
Bibliographic record
Abstract
PURPOSE: This article aims to document the process the province of Newfoundland and Labrador used to develop an innovative Physician Management and Leadership Program (PMLP). The PMLP is a collaborative initiative among Memorial University (Faculty of Medicine and Faculty of Business), the Government of Newfoundland and Labrador, and the Regional Health Authorities. As challenges facing health-care systems become more complex there is a growing need for management and leadership training for physicians. DESIGN/METHODOLOGY/APPROACH: Memorial University Faculty of Medicine and the Gardiner Centre in the Faculty of Business in partnership with Regional Health Authorities and the Government of Newfoundland and Labrador identified the need for a leadership and management education program for physician leaders. A provincial needs assessment of physician leaders was conducted to identify educational needs to fill this identified gap. A Steering Committee was formed to guide the design and implementation and monitor delivery of the 10 module Physician Management and Leadership Program (PMLP). FINDINGS: Designing management and leadership education programs to serve physicians who practice in a large, predominately rural geographic area can be challenging and requires efficient use of available resources and technology. ORIGINALITY/VALUE: While there are many physician management and leadership programs available in Canada and abroad, the PMLP was designed to meet the specific educational needs of physician leaders in Newfoundland and Labrador.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".