Integrated Care Planning for Cancer Patients: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: There has been a growing emphasis on the use of integrated care plans to deliver cancer care. However little is known about how integrated care plans for cancer patients are developed including featured core activities, facilitators for uptake and indicators for assessing impact. METHODS: Given limited consensus around what constitutes an integrated care plan for cancer patients, a scoping review was conducted to explore the components of integrated care plans and contextual factors that influence design and uptake. RESULTS: Five types of integrated care plans based on the stage of cancer care: surgical, systemic, survivorship, palliative and comprehensive (involving a transition between stages) are described in current literature. Breast, esophageal and colorectal cancers were common disease sites. Multi-disciplinary teams, patient needs assessment and transitional planning emerged as key features. Provider buy-in and training alongside informational technology support served as important facilitators for plan uptake. Provider-level measurement was considerably less robust compared to patient and system-level indicators. CONCLUSIONS: Similarities in design features, components and facilitators across the various types of integrated care plans indicates opportunities to leverage shared features and enable a management lens that spans the trajectory of a patient's journey rather than a phase-specific silo approach to 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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".