Evaluating a Chronic Disease Management Improvement Collaboration: Lessons in Design and Implementation Fundamentals
Bibliographic record
Abstract
Evaluating a Chronic Disease Management Improvement Collaboration ABstrActFor the Canadian Foundation for Healthcare Improvement (CFHI), the Atlantic Healthcare Collaboration (AHC) was a pivotal opportunity to build upon its experience and expertise in delivering regional change management training and to apply and refine its evaluation and performance measurement approach.This paper reports on its evaluation principles and approach, as well as the lessons learned as CFHI diligently coordinated and worked with improvement project (IP) teams and a network of stakeholders to design and undertake a suite of evaluative activities.The evaluation generated evidence and learnings about various elements of chronic disease prevention and management (CDPM) improvement processes, individual and team capacity building and the role and value of CFHI in facilitating tailored learning activities and networking among teams, coaches and other AHC stakeholders.résumé Dans le cas de la Fondation canadienne pour l'amélioration des services de santé (FCASS), la Collaboration des organismes de santé de l'Atlantique (COSA) était une occasion décisive de mettre à profit sa vaste expertise et sa riche expérience de formation en matière de gestion de changements régionaux, en plus d'affiner son approche d'évaluation et de mesure du rendement.Cet article porte sur les principes et l'approche de cette évaluation, ainsi que sur les enseignements tirés de l'expérience de la FCASS tandis que celle-ci travaillait diligemment à coordonner les activités de la Collaboration et à soutenir les équipes des projets d'amélioration (PA), ainsi que le réseau des parties prenantes concernées, dans le but de concevoir et d'entreprendre une série d'activités d'évaluation qui permettraient de produire des données probantes et des enseignements concernant divers éléments des processus d 'amélioration de la prévention et de la gestion des maladies chroniques (PGMC), ainsi que le renforcement des capacités individuelles et d'équipe.En outre, il s'agissait d'évaluer le
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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 | 0.383 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.011 | 0.016 |
| 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".