Healthcare providers’ perspectives on care coordination for adults with cancer and multiple chronic conditions: a systematic review
Bibliographic record
Abstract
Inherent treatment complexities for patients with both cancer and multiple chronic conditions (MCC) make these patients likely candidates for shared care between primary care providers (PCPs) and oncologists. However, providers’ views on the optimal model for care coordination between PCPs and oncologists in the context of both cancer and MCC are unclear. Thus, the purpose of this systematic review is to evaluate the perceptions of PCPs and oncologists regarding barriers and facilitators to care coordination during the care of patients with cancer and MCC, and their opinions on what is needed to improve current care coordination strategies. We systematically searched PubMed, CINAHL and PsycINFO for articles pertaining to PCPs’ and oncologists’ perspectives, experiences and needs regarding care coordination during the cancer care continuum, in the context of patients with cancer and MCC. A total of 22 articles were retained. From qualitative synthesis, three themes emerged regarding PCPs’ and oncologists’ perceived barriers to cancer care coordination: (1) limited findings of physicians’ experiences in MCC care; (2) lack of defined provider roles in cancer care; and (3) lack of comprehensive information sharing, efficient communication methods and clear shared-care plans during care for cancer patients with MCC. Results provide insights into providers’ needs for navigating the complexities of cancer care coordination. Future studies should consider further investigating the needs of patients and multiple provider types for optimizing care coordination throughout the cancer care continuum.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".