Role of mandated structures in promoting integration within and between levels: longitudinal analysis of deliberate actions in Quebec's cancer network
Bibliographic record
Abstract
Introduction: Healthcare networks are mandated structures promoted by governing authorities as a means of assuring effective and integrated care. Network governance models influence the translation of policy intentions into change at organizational and practice level, with formal and informal coordination arrangements playing a role. This study identifies deliberate actions taken within a national cancer network and their influence on making integrated care a reality within and across decision-making levels and provider groups. Objectives: The Quebec Cancer network, instituted in 1998, provides a rare opportunity to empirically probe deliberate actions taken over a long period within a network. The aim is to understand how these drive the emergence of principled engagement, mutual understanding and capacity for joint action that are recognized in seminal work by Emerson, Nabatchi and Balogh, as mechanisms of collaborative governance. The study provides guidance for health system leaders and managers in their efforts to support change towards network integration in specialized domains. Methods: We conducted a longitudinal qualitative case study of the Quebec cancer network with data drawn from documents, meeting observation and individual interviews with stakeholders (n=37), including patient partners, involved in regional and/or national cancer network structures. We examined the formal structures imposed by central cancer authorities, the perceptions of network actors at multiple levels on the collaborative dynamics between actors within these structures, and their impact on integrated practice. Results: We find that mandated structures imposed by central cancer authorities provide important venues for the activation of mechanisms that underpin collaborative governance. Opportunities to develop trust and recognize each other’s distinct contributions and interdependencies are important drivers of these mechanisms. Findings also highlight that these structures involve trade-offs between cohesiveness and inclusivity across care sites and provider groups. Finally, they highlight the importance of routes that allow local initiatives and innovations to filter up into central policy decisions, and junction points where descending and ascending movements can converge to develop mutual understanding and resolve controversies. Implications: Insights from this study will guide system leaders in their efforts to create network governance conditions for the emergence of integrated high-quality care across the cancer trajectory. Findings suggest pathways for further inquiry, notably research the into trade-offs between cohesiveness and inclusivity in mandated network structures that may hold lessons for policy in cancer care and other specialized domains.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".