Interventions to Support Transitions in Care Among Patients With Cancer: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The cancer journey from diagnosis through survivorship is complex and involves care from many healthcare providers across a variety of settings. Navigating the transitions between care providers and settings can be improved through interventions. The objective of this study was to map and characterize evidence on interventions to improve transitions in care among patients with cancer. METHOD: Six databases were searched to identify relevant studies that described or evaluated interventions to support transitions in care for patients with cancer. Data on the interventions, the type of transition in care, type of cancer, and outcomes (including measure of effectiveness) were abstracted. Data were synthesized and analyzed using descriptive statistics. RESULT: Of the 38,876 data sources identified, 150 were included. Most included studies were from the United States and were observational studies exploring interventions to facilitate the transition from treatment to survivorship (followed by interventions for the transition from hospital to home) among patients with breast cancer (followed by gastrointestinal cancers, lung cancers, and hematologic cancers). Interventions that were found to be effective were most commonly those that facilitated the transition from diagnosis to treatment and for the transition from hospital to home. CONCLUSION: This comprehensive synthesis is an important resource for those trying to improve transitions in care for patients living with and beyond cancer. Despite the large body of evidence identified, gaps remain; there is a paucity of studies exploring transitions in care during cancer treatment and among some cancers (e.g., brain tumors, head and neck, pancreatic).
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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 | 0.016 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".