Améliorer le leadership dans les services de santé au Canada
Bibliographic record
Abstract
Building Better Health Care Leadership for Canada explains the development and implementation of the Executive Training in Research Application (EXTRA) program. Managed and funded by the Canadian Health Services Research Foundation in partnership with the Canadian Medical Association, the Canadian Nursing Association, and the Canadian College of Health Care executives, EXTRA is a two-year national fellowship program that uses the principles of adult learning theory as well as practical projects to educate senior health care leaders in making more consistent use of research evidence in their management roles. Fellows apply the theory learned in residency sessions and educational activities to projects within their home organizations. The authors identify the imperative for better use of evidence, outline the core elements of the curriculum, and capture the real-world experience of regional leaders and fellows involved in making specific changes informed by research-based evidence within their organization. Contributors include Jean-Louis Denis (École nationale d'administration publique), Terrence Sullivan (Cancer Care Ontario), Owen Adams (Canadian Medical Association), Malcolm Anderson (Queen's University), Lynda Atack, Robert Bell (University Health Network), Sam G Campbell (Queen Elizabeth II Health Sciences Centre), Sylvie Cantin (Régie régionale de la santé et des services sociaux de la Montérégie), Ward Flemons (Calgary Health Region), Dorothy Forbes, J. Sonja Glass (Grey Bruce Health Services), Paula Goering (Centre for Addiction & Mental Health, Toronto), Karen Golden-Biddle (Boston University School of Management), Jeffrey S. Hoch (University of Toronto), Paul Lamarche (Université de Montréal), Ann Langley (École des hautes études commerciales), John N. Lavis (McMaster University), Jonathan Lomas (Canadian Health Services Research Foundation), Margo Orchard (Ministry of Health and Long Term Care, Ontario), Raynald Pineault (University of Montreal), Brian D. Postl (Winnipeg Regional Health Authority), Christine Power (Capital District Health Authority, Halifax), Trish Reay (University of Alberta), Jean Rochon (National Public Health Institute of Quebec), Denis A. Roy (Agence de la santé et des services sociaux de la Montérégie Longueuil), Andrea Seymour (Government of New Brunswick), Samuel B. Sheps (University of British Columbia), Micheline Ste-Marie (McGill University Health Centre), Nina Stipich (Canadian Health Services Research Foundation), David Streiner (Baycrest Centre for Geriatric Care, Toronto), Carl Taillon (Centre hospitalier universitaire de Québec), and Muriah Umoquit (Cancer Care Ontario).
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".