Enhancing inter-organisational partnerships in integrated care models for older adults: a multiple case study
Bibliographic record
Abstract
PURPOSE: The purpose of this paper was to develop deeper insights into the practices enacted by entrepreneurial healthcare managers to enhance the implementation of a partnership logic in integrated care models for older adults. DESIGN/METHODOLOGY/APPROACH: A multiple case study design in two urban centres in two jurisdictions in Canada, Ontario and Quebec. Data collection included 65 semi-structured interviews with policymakers, managers and providers and analysis of key policy documents. The institutional entrepreneur theory provided the theoretical lens and informed a reflexive iterative data analysis. FINDINGS: While each case faced unique challenges, there were similarities and differences in how managers enhanced a partnership's institutional logic. In both cases, entrepreneurial healthcare managers created new roles, negotiated mutually beneficial agreements and co-located staff to foster inter-organisational partnerships between public, private and community organisations in the continuum of care for older adults. In addition, managers in Ontario secured additional funding, while managers in Quebec organised biannual meetings and joint training to enhance inter-organisational partnerships. ORIGINALITY/VALUE: This study has two main implications. First, efforts to enhance inter-organisational partnerships should strategically include institutional entrepreneurs. Second, successful institutional changes may be supported by investing in integrated implementation strategies that target roles of staff, co-location and inter-organisational agreements.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".