Collaborative governance in the Quebec Cancer Network: a realist evaluation of emerging mechanisms of institutionalization, multi-level governance, and value creation using a longitudinal multiple case study design
Bibliographic record
Abstract
Abstract Background People living with and beyond cancer (PLC) receive various forms of specialty care at different locations and many interventions concurrently or over time. They are affected by the operation of professional and organizational silos. This results in undue delays in access, unmet needs, sub-optimal care experiences and clinical outcomes, and human and financial costs for PLCs and healthcare systems. National cancer control programs advocate organizing in a network to coordinate actions, solve fragmentation problems, and thus improve clinical outcomes and care experiences for every dollar invested. The variable outcomes of such networks and factors explaining them have been documented. Governance is the “missing link” for understanding outcomes. Governance refers to the coordination of collective action by a body in a position of authority in pursuit of a common goal. The Quebec Cancer Network (QCN) offers the opportunity to study in a natural environment how, why, by whom, for whom, and under what conditions collaborative governance contributes to practices that produce value-added outcomes for PLCs, healthcare providers, and the healthcare system. Methods/design The study design consists of a longitudinal case study, with multiple nested cases (4 local networks nested in the QCN), mobilizing qualitative and quantitative data and mixed data from various sources and collected using different methods, using the realist evaluation approach. Qualitative data will be used for a thematic analysis of collaborative governance. Quantitative data from validated questionnaires will be analyzed to measure relational coordination and teamwork, care experience, clinical outcomes, and health-related health-related quality of life, as well as a cost analysis of service utilization. Associations between context, governance mechanisms, and outcomes will be sought. Robust data will be produced to support decision-makers to guide network governance towards optimized clinical outcomes and the reduction of the economic toxicity of cancer for PLCs and health systems.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".