Encouraging teamwork and innovative practices by creating proximity: A qualitative study of continuous improvements in the Quebec Cancer Network.
Bibliographic record
Abstract
e13509 Background: Quality cancer care relies on each profession keeping up with advances and best practices and spreading these across a complex multi-team system1. It requires enabling multiple providers and people living with cancer to bridge the distance between them and complement each other's contributions. The proximity framework2 provides a valuable way to understand conditions that increase the likelihood of knowledge sharing, innovation and collaboration. Methods: A qualitative study design of the Quebec Cancer Network was undertaken, with data collected from interviews with policymakers, managers, providers and users (N=22), observation of national and local level meetings (N=28) and document review. Interpretive Description using content analysis sought to identify actions that created proximity dimensions and the perceived influence these had on the development and spread of new approaches. Results: Deliberate actions taken within the network created different dimensions of proximity that impacted teamwork. Prescriptions from network leadership – including consistent promotion of the National cancer plan, patient participation in governance structures, shared quality indicators, and establishment of multidisciplinary committees at local level, created cognitive proximity: a shared mental model emphasizing patient-centred care and organizational proximity: shared standards across the network. Support for professional communities of practice created relational and institutional proximity, increasing trust and knowledge sharing. Local committees enhanced relational and cognitive proximity as providers came to appreciate and optimize each other's contribution to care. Conclusions: The combination of proximity dimensions created through communities of practice and prescriptions from the national level help develop and spread improvements that are tailored to - and take advantage of - networked team-based cancer care delivery. This reflects a balanced proximity where communities of practice pursue new knowledge and innovative practices that can be introduced in local committees to see how it fits with other contributions to solving a problem, thereby promoting recognition of interdependency within and between teams. Synergy between actions is essential to enhancing proximity. The proximity framework offers a complementary perspective to better understand opportunities for improving models of care. References: 1Weaver, S. J., et al. (2018). Unpacking care coordination through a multiteam system lens. Medical care, 56(3), 247-259. et al. Unpacking Care Coordination Through a Multiteam System Lens. Medical Care. 2018;56(3):247-59. 2Knoben, J., & Oerlemans, L. A. (2006). Proximity and inter‐organizational collaboration: A literature review. I nternational Journal of management reviews, 8(2), 71-89.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".