Collaborative governance in integrated healthcare networks: insights from the cancer program in Québec (Canada)
Bibliographic record
Abstract
Context: The challenge of modernizing health services delivery for cancer patients and their loved ones involves moving towards integrated network-based practices. While initiatives at different levels of healthcare systems contribute to this movement, further efforts are needed to maintain and extend gains to adapt to the complex and fast-changing cancer environment. A collaborative governance regime, understood as a form of capacity building for joint action across boundaries, represents a promising way to improve and sustain integrated care. Objective: This presentation reports insights from providers and users of health services gained from the experience in operationalizing an integrated network-of-networks in the context of a national cancer program in Quebec (Canada). Methods: Methods: Insights are drawn from a larger multiple case study that took place from 2018 to 2021. Participants (n=39) are knowledgeable informants on how “mandated” integration structures and processes support collaboration among the various components of the network. Documents (n=45) related to network governance and integrated practices are also included. The data collection guide and analysis based on qualitative interpretive description are framed on the Collaborative Governance framework to capture critical dimensions in the integration process. These include clear mission, common and shared definition of complex problems and potential solutions, and identification of common values to support motivation, engagement and capacity for joint action. Results: We report on three main tensions related to integration in the cancer network that suggest strategies for enhancing collaborative governance: The first tension emerges as some participants perceive “mandated integration” within a “prescribed collaborative work” as a constraint that undermines previous collaborative work at local level. This tension leads to unresolved controversies within and between actor groups, weakening the mechanisms of collaborative governance. The second tension is a principle-to-practice divide around engaging people affected by cancer in network governance. Despite widespread motivation, participation at the local level varies according to managers' willingness and skill in creating open and inclusive processes where evidence-based medical practice and lived patient experience can be assembled. This tension is seen to contribute to a poor consideration of patient perspectives during the highly volatile period of decision-making during the COVID-19 pandemic. The third tension involves the hierarchy of cancer services promoted in the national cancer program and the standardization of practices. Organizing the network-of- networks by cancer specialization involves creating trajectories by type of cancer and their navigation according to established standards of practice and quality considerations. The consequent redistribution of human and financial resources, and revised ownership of care process creates perceived asymmetries between cancer network professionals and centers and controversies that hinder integrated practices. Conclusion: Despite favorable starting conditions from the national cancer program and its central leadership promoting collaborative governance, tensions that emerge through the pursuit of network integration limit the transition to a more collaborative regime. Taking the time to work out these tensions as integration proceeds appears essential to arrive at a governance model that is appropriate and acceptable for all network members.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".