Determinants of survivorship care plan implementation in U.S. cancer programs.
Bibliographic record
Abstract
78 Background: The Institute of Medicine recommended and many professional societies require survivorship care plan (SCP) use to facilitate cancer survivors’ transition from treatment to follow-up care. Rates of SCP adoption (plans to use SCPs) and implementation (current use) in US cancer programs remain unclear. Our objectives were to (1) assess rates of SCP adoption and implementation and (2) determine what distinguishes cancer programs that have implemented SCPs from those that have not moved beyond adoption. Methods: We surveyed employees knowledgeable about SCP adoption and implementation in a nationally representative sample of 100 US cancer programs. Data were analyzed using descriptive and bivariate statistics. Results: The response rate was 80%. Ninety-six percent of programs adopted SCPs, but only 45% implemented SCPs. Among programs that implemented, SCP use remains inconsistent: Use is restricted primarily to breast (81.58%) and colorectal (55.26%) cancer survivors; in 58.33% of these programs, less than a quarter of providers has ever used SCPs; and SCPs are seldom delivered to survivors or their primary care providers. Employees in many programs indicated that SCPs were adopted because of the belief that SCPs would improve care quality and the release of professional society guidelines; however, neither of these factors influenced SCP implementation. Few quality markers (e.g., NCI-designated program type; Commission on Cancer membership) influenced SCP implementation. Determinants of SCP implementation included teaching hospital program type (p = .04) and NCCCP membership (p = .009). Freestanding facility type had a negative relationship with SCP implementation (p = .02). Conclusions: Given inconclusive evidence of SCPs’ effectiveness in improving care coordination and patient outcomes, many scholars have recently advocated for research to promote SCPs’ effectiveness. These efforts may be in vain if SCPs are not more routinely implemented. Efforts should be targeted at enabling programs to implement quality improvement tools. Future research should determine what promotes SCP implementation among teaching hospitals and NCCCP members, and what inhibits freestanding facilities from implementing SCPs.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".