The role of collaborative governance in translating national cancer programs into network-based practices: A longitudinal case study in Canada
Bibliographic record
Abstract
OBJECTIVES: Networks (multiple organizations or actors coordinating their activities towards a common goal) have been promoted in the cancer programs of a number of countries. But there is little empirical evidence on whether and how they overcome the siloed functioning endemic in specialized domains. This study examines how collaborative governance takes shape to support integrated network-based practices within a prescribed national cancer program. METHODS: = 45) generated at national and regional level. Abductive thematic analysis during and post-field work was based on Emerson's collaborative governance framework. It aimed to identify how collaborative governance mechanisms (principled engagement, shared motivation and capacity for joint action) were activated in the network, and their contribution to translating a national cancer program into network-based practices at the point of care. RESULTS: Principled engagement was driven through interdisciplinary committees at national and regional level, communities of practice and trajectory-development efforts. These mandated structures supported knowledge exchange and contributed to the recognition of interdependencies, distribution of leadership and development of mutual understanding and trust. Shared motivation benefitted from a vision of patient-centred care but was hindered by top-down communication vehicles that did not allow regional priorities to filter upwards to central level. Between care providers in different settings, trust and candidacy were identified as mechanisms important to shared motivation, though network actions did not sufficiently support trust across care settings, or even between members of the same profession. Candidacy issues hindered family physician participation in cancer network structures that mirrored ongoing difficulties to including them in cancer care practice. Institutional arrangements were important drivers of capacity for joint action in the network. Common indicators were recognized as important to generating efforts towards common goals; however, questions around their validity reduced their contribution to capacities for joint action. CONCLUSIONS: 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 practice. Taking the time to work out these tensions as integration proceeds in waves 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".