Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a case study demonstrating that LEADS in a Caring Environment Capabilities Framework in Canada can assist physicians to be partners in leading health reform. Design/methodology/approach A descriptive case-based approach was followed, relying on existing documents, research papers and peer-reviewed articles, to substantiate the effect of LEADS on physician leadership in Canada. Findings The Canadian LEADS framework enables physicians to lead by providing them with access to best practices of leadership, acting as an antidote to fragmented leadership practice, setting standards for development and accountability and providing opportunities for efficient and effective system-wide leadership development and change. Research limitations/implications A formal systematic review of the literature was not conducted. Findings can only be generalized to other cases if the reader sees contextual similarities between the present study context and the other case's context. Practical implications This case demonstrates that national leadership frameworks have a role in facilitating physician leadership. Other national jurisdictions may wish to explore the Canadian case to determine how to use a common leadership language to engage physicians in health reform. Social implications Leadership is a key component of health reform. A common language and set of standards (LEADS) that can engage physicians will benefit patients and citizens in Canada. Originality/value This national case study shows how a nationally endorsed leadership framework such as LEADS can facilitate better physician leadership for health reform.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".