Challenges and Insights in Implementing Coordinated Care between Oncology and Primary Care Providers: A Canadian Perspective
Bibliographic record
Abstract
We report here on the current state of cancer care coordination in Canada and discuss challenges and insights with respect to the implementation of collaborative models of care. We also make recommendations for future research. This work is based on the findings of the Canadian Team to Improve Community-Based Cancer Care Along the Continuum (canimpact) casebook project. The casebook project identified models of collaborative cancer care by systematically documenting and analyzing Canadian initiatives that aim to improve or enhance care coordination between primary care providers and oncology specialists. The casebook profiles 24 initiatives, most of which focus on breast or colorectal cancer and target survivorship or follow-up care. Current key challenges in cancer care coordination are associated with establishing program support, engaging primary care providers in the provision of care, clearly defining provider roles and responsibilities, and establishing effective project or program planning and evaluation. Researchers studying coordinated models of cancer care should focus on designing knowledge translation strategies with updated and refined governance and on establishing appropriate protocols for both implementation and evaluation.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| 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".