The challenge of optimizing supports for people living with and beyond cancer: creating proximity between cancer and non-profit community-based providers
Bibliographic record
Abstract
PURPOSE: Non-profit community-based organizations (CO) remain insufficiently integrated into cancer networks. Drawing on dimensions of proximity, this study explores how and why coordination between cancer teams and COs is established and solidified. METHODS: A descriptive interpretive study is undertaken in Québec (Canada), where a cancer program has long promoted the integration of COs in the cancer trajectory. Semi-directed interviews with providers, managers and people living with and beyond cancer (total n = 46) explore the challenges of coordination between cancer and CO providers, along with facilitating or impeding factors. Three main themes related to coordination in cancer networks emerge, which are analyzed by operationalizing the multi-dimensional framework of proximity. RESULTS: Findings reveal a lack of cognitive proximity, which calls for efforts to both identify patient needs and increase cancer team knowledge and appreciation of CO resources. Organizational proximity refers to systems and rules that facilitate interactions, and we find that referral mechanisms and communication channels are inadequate, with patients often playing a linking role despite barriers. Coordination improves when relational proximity is established between cancer and CO teams, and this can be enhanced by geographic proximity; in one region, COs have a physical presence within the cancer center. CONCLUSION: Integrating COs into the cancer network can help meet the spectrum of needs faced by people living with and beyond cancer. This study offers managers and decision-makers insight into how coordination between cancer teams and COs can be supported. Proximity allows the distinct contributions of actors to be considered in context and contributes to understanding the "how" of integrated practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".