Implementation of the Acute Care for Elders Strategy to Improve the Quality of Care Transitions in Quebec and Ontario: a Retrospective Multiple Case Study
Bibliographic record
Abstract
Background: In 2016, two Canadian hospitals participated in a quality improvement (QI) program, the International Acute Care for Elders (ACE) Collaborative, and sought to adapt and implement a transition coach intervention (TCI). Both hospitals were challenged to provide optimal continuity of care for an increasing number of older adults. The two hospitals received initial funding, coaching, educational materials, and tools to adapt the TCI to their local contexts, but the QI project teams achieved different results. We aimed to compare the implementation of the ACE TCI in these two Canadian hospitals to identify the factors influencing the adaptation of the intervention to the local contexts and to understand their different results. Methods: We conducted a retrospective multiple case study, including documentary analysis, 21 semi-structured individual interviews, and two focus groups. We performed thematic analysis using a hybrid inductive-deductive approach. Results: Both hospitals met initial organizational goals to varying degrees. Our qualitative analysis highlighted certain factors that were critical to the effective implementation and achievement of the QI project goals: the magnitude of changes and adaptations to the initial intervention; the organizational approaches to the QI project implementation, management, and monitoring; the organizational context; the change management strategies; the ongoing health system reform and organizational restructuring. Our study also identified other key factors for successful care transition QI projects: minimal adaptation to the original evidence-based intervention; use of a collaborative, bottom-up approach; use of a theoretical model to support sustainability; support from clinical and organizational leadership; a strong organizational culture for QI; access to timely quality measures; financial support; use of a knowledge management platform; and involvement of an integrated research team and expert guidance. Conclusion: Many of the lessons learned and strategies identified from our analysis will help clinicians, managers, and policymakers better address the issues and challenges of adapting evidence-based innovations in care transitions for older adults to local contexts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".