Supporting Models to Transition Breast Cancer Survivors to Primary Care: Formative Evaluation of a Cancer Care Ontario Initiative
Bibliographic record
Abstract
PURPOSE: Many breast cancer (BC) survivors continue to be seen by specialists for routine follow-up care despite growing evidence that transitioning appropriate BC survivors to primary care is safe and effective. We describe the formative evaluation of an initiative involving the development and implementation of sustainable models of follow-up care for BC survivors across 14 Regional Cancer Centers (RCC) in Ontario, Canada. METHODS: After extensive consultation, each RCC received catalyst funding for the initiative. Detailed work plans were developed locally and submitted to Cancer Care Ontario. Each region had a designated lead and support from primary care. Funding could be used to develop any aspect of the model. Formative evaluation of each model was conducted with descriptive analysis of the model created, including summative description of how resources were used, the number of survivors transitioned, and preliminary results from patient surveys of experience at transition. RESULTS: Each region developed a unique model that included clearly identified structures and processes of care. All regions used survivorship care plans and patient education materials. Three main models of follow-up care were developed: (1) direct to primary care, (2) transition clinic, and (3) shared care. A total of 3,418 BC survivors transitioned between March 2012 and September 2013. Patient experience surveys were distributed by 12 regions, gathering responses from 752 BC survivors, with 85% reporting that they felt adequately prepared for the transition. CONCLUSION: Using the approach described, wide-scale transition of appropriate BC survivors from oncology-led practice is feasible over a fairly short timeframe.
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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 | 0.095 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".