Patient participation in cancer network governance: a six-year case study
Bibliographic record
Abstract
BACKGROUND: Patient participation in decision-making has become a hallmark of responsive healthcare systems. Cancer networks in many countries have committed to involving people living with and beyond cancer (PLC) at multiple levels. However, PLC participation in network governance remains highly variable for reasons that are poorly understood. This study aims to share lessons learned regarding mechanisms that enable PLC participation in cancer network governance. METHODS: This multiple case study, using a qualitative approach in a natural setting, was conducted over six years in three local cancer networks within the larger national cancer network in Quebec (Canada), where PLC participation is prescribed by the Cancer Directorate. Data were collected from multiple sources, including individual and focus group interviews (n = 89) with policymakers, managers, clinicians and PLC involved in national and local cancer governance committees. These data were triangulated and iteratively analysed according to a framework based on functions of collaborative governance in the network context. RESULTS: We identify three main mechanisms that enable PLC participation in cancer network governance: (1) consistent emphasis on patient-centred care as a network objective; (2) flexibility, time and support to translate mandated PLC representation into meaningful participation; and (3) recognition of the distinct knowledge of PLC in decision-making. The shared vision of person-centred care facilitates PLC participation. The quality of participation improves through changes in how committee meetings are conducted, and through the establishment of a national committee where PLC can pool their experience, develop skills and establish a common voice on priority issues. PLC knowledge is especially valued around particular challenges such as designing integrated care trajectories and overcoming barriers to accessing care. These three mechanisms interact to enable PLC participation in governance and are activated to varying extents in each local network. CONCLUSIONS: This study reveals that mandating PLC representation on governance structures is a powerful context element enabling participation, but that it also delineates which governance functions are open to influence from PLC participation. While the activation of mechanisms is context dependent, the insights from this study in Quebec are transferable to cancer networks in other jurisdictions seeking to embed PLC participation in decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".