The Leadership and Organizational Context Required to Support Patient Partnerships
Bibliographic record
Abstract
Healthcare providers and managers typically design programs based on what they believe patients need and want.Yet patients have knowledge and insight into how the system can be changed to better meet their needs, improve outcomes and reduce costs.We describe challenges in creating a culture of patient partnerships and the leadership actions and organizational context required now and in the future to support engagement-capable environments at the organizational and policy levels in Canada.Case examples illustrate the need for leaders to set clear expectations, develop the infrastructure to support patient partnerships and provide education to staff, physicians and patient partners.Résumé Les prestataires et gestionnaires de soins de santé conçoivent généralement des programmes selon une conception théorique des besoins et des volontés du patient.Pourtant, le patient a des idées et des connaissances par rapport aux éléments du système qui gagneraient à être changés pour mieux répondre à ses besoins, améliorer ses résultats et réduire le coût des soins.Cet article décrit les défis liés à la création d'une culture de partenariat avec le patient pour aujourd'hui et demain, les actions que doivent prendre les dirigeants et le contexte organisationnel nécessaire pour instaurer des milieux propices à l'engagement aux niveaux organisationnel et politique au Canada.Les exemples de cas illustrent la nécessité pour les dirigeants d'établir des attentes claires, d'aménager l'infrastructure nécessaire pour soutenir le partenariat avec le patient et de former le personnel, les médecins et les patients partenaires.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".