Understanding cancer networks better to implement them more effectively: a mixed methods multi-case study
Bibliographic record
Abstract
BACKGROUND: Managed cancer networks are widely promoted in national cancer control programs as an organizational form that enables integrated care as well as enhanced patient outcomes. While national programs are set by policy-makers, the detailed implementation of networks is delegated at the service delivery and institutional levels. It is likely that the capacity to ensure more integrated cancer services requires multi-level governance processes responsive to the strengths and limitations of the contexts and capable of supporting network-based working. Based on an empirical case, this study aims to analyze the implementation of a mandated cancer network, focusing on governance and health services integration as core concepts in the study. METHODS/DESIGN: This nested multi-case study uses mixed methods to explore the implementation of a mandated cancer network in Quebec, a province of Canada. The case is the National Cancer Network (NCN) subdivided into three micro-cases, each defined by the geographic territory of a health and social services region. For each region, two local health services centers (LHSCs) are selected based on their differences with respect to determining characteristics. Qualitative data will be collected from various sources using three strategies: review of documents, focus groups, and semi-directed interviews with stakeholders. The qualitative data will be supplemented with a survey that will measure the degree of integration as a proxy for implementation of the NCN. A score will be constructed, and then triangulated with the qualitative data, which will have been subjected to content analysis. Qualitative, quantitative, and mixed methods data will be interpreted within and across cases in order to identify governance patterns similarities and differences and degree of integration in contexts. DISCUSSION: This study is designed to inform decision-making to develop more effective network implementation strategies by thoroughly describing multi-level governance processes of a sample of settings that provide cancer services. Although the study focuses on the implementation of a cancer network in Quebec, the rich descriptions of multiple nested cases will generate data with a degree of generalizability for health-care systems in developed countries.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".