Trial and error, together: divergent thinking and collective learning in the implementation of integrated care networks
Bibliographic record
Abstract
Hybrid networks that link disparate professionals and organizations are a common approach to deliver integrated care to patients. Recent literature argues that successful implementation of these networks demands a socio-cognitive perspective in which stakeholder mental frames and thought processes are prioritized, investigated, and compared. The aims of this article are to identify where mindsets diverge among clinical and managerial stakeholders involved in the implementation of integrated care networks known as ‘Health Links’ (HLs) in Ontario, Canada, and to describe strategies to support stakeholders’ capacity to collectively learn and develop more convergent views. Drawing from shared mental model theory and practice-based learning theory, a secondary analysis was conducted of interview data with 55 healthcare professionals and managers involved in the implementation of HLs. We identified examples of divergences in stakeholders’ conceptualization of the HL design and approach (‘strategy mental model’) and their perceptions of each other and how they work together (‘relationship mental model’). We also identified four strategies that facilitate learning and possibly mental model convergence. The results of the study may help guide stakeholder dialogue towards collective learning and coordinated action for integrated care delivery.Points for practitionersThe findings suggest that in the implementation of large-scale change involving multiple stakeholder groups, there are predictable areas where divergent views are likely to occur and may have a negative impact on coordinated action. An awareness of these potential divergences can guide practitioners to examine them explicitly and regularly, and to proactively develop strategies to support practice-based learning and the development of a convergent perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".