Implementing Survivorship Care Plans for Colon Cancer Survivors
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To evaluate the feasibility, usability, and satisfaction of a survivorship care plan (SCP) and identify the optimum time for its delivery during the first 12 months after diagnosis. DESIGN: Prospective, descriptive, single-arm study. SETTING: A National Cancer Institute-designated cancer center in the southeastern United States. SAMPLE: 28 nonmetastatic colon cancer survivors within the first year of diagnosis and their primary care physicians (PCPs). METHODS: Regular screening identified potential participants who were followed until treatment ended. An oncology certified nurse developed the JourneyForward™ SCP, which then was delivered to the patient by the oncology nurse practitioner (NP) during a routine follow-up visit and mailed to the PCP. MAIN RESEARCH VARIABLES: Time to complete, time to deliver, usability, and satisfaction with the SCP. FINDINGS: During one year, 75 patients were screened for eligibility, 34 SCPs were delivered, and 28 survivors and 15 PCPs participated in the study. It took an average of 49 minutes to complete a surgery SCP and 90 minutes to complete a surgery plus chemotherapy SCP. Most survivors identified that before treatment ended or within the first three months was the preferred time to receive an SCP. CONCLUSIONS: The SCPs were well received by the survivors and their PCPs, but were too time and labor intensive to track and complete. IMPLICATIONS FOR NURSING: More work needs to be done to streamline processes that identify eligible patients and to develop and implement SCPs. Measuring outcomes will be needed to demonstrate whether SCPs are useful or not.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".