Integrating primary care and cancer care in survivorship: A collaborative approach.
Bibliographic record
Abstract
103 Background: Primary care providers (PCPs) have an important role in the provision of survivorship care. While there is evidence to support the feasibility and safety of PCP-led survivorship care, there are gaps in knowledge about how to best integrate providers to support transitions, enhance quality of care, increase system efficiencies, and improve patient and provider satisfaction. Methods: A pan-Canadian study comprised of three projects has been initiated to address three key aspects of care integration, based on a previously described system performance framework. Functional integration will be studied through the evaluation of electronic survivorship care plans using a prospective cohort of breast and colorectal cancer patients with pre and post measures of knowledge, care coordination, and satisfaction. Vertical integration will be evaluated through a series of descriptive case studies to document structures and processes that are currently in place to support PCP re-referral to regional cancer centres. Clinical integration will be studied through the development and evaluation of an interspecialty survivorship training curriculum for oncology and family medicine trainees. Results: Functional integration: Development of an electronic platform for care plan outputs is complete. Two sites in Ontario (ON) and one in British Columbia (BC) have been selected to study the impact on 200 patients and their providers. Vertical integration: Using a study-specific interview guide, 48 semi-structured key informant interviews have been successfully conducted in ON; 15 interviews are planned for Manitoba (MB) and 15 for BC. Clinical Integration: a National Advisory Committee was established and needs assessments were performed with postgraduate program directors, cancer survivors, and trainees using online surveys and focus groups. A blended learning curriculum is being piloted in MB, ON, and BC in 2015. Conclusions: Integrating primary care and cancer care in survivorship requires a collaborative approach that begins in residency, supports PCPs with clear mechanisms for re-entry, and optimizes communication. This study will inform approaches to enhancing provider integration and survivorship care.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 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".