Enhancing Survivorship Care Planning for Patients With Localized Prostate Cancer Using a Couple-Focused mHealth Symptom Self-Management Program: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: This project explores a new model of care that enhances survivorship care planning and promotes health for men with localized prostate cancer transitioning to posttreatment self-management. Survivorship care planning is important for patients with prostate cancer because of its high incidence rate in the United States, the frequent occurrence of treatment-related side effects, and reduced quality of life (QOL) for both men and their partners. A key component of comprehensive survivorship care planning is survivorship care plans (SCPs), documents that summarize cancer diagnosis, treatment, and plans for follow-up care. However, research concerning the effectiveness of SCPs on patient outcomes or health service use has thus far been inconclusive. SCPs that are tailored to individual patients' needs for information and care may improve effectiveness. OBJECTIVE: This study aims to examine the feasibility of an enhanced survivorship care plan (ESCP) that integrates a symptom self-management mHealth program called Prostate Cancer Education and Resources for Couples (PERC) into the existing standardized SCP. The specific aims are to (1) examine the feasibility of delivering ESCPs and (2) to estimate the magnitude of benefit of ESCPs. METHODS: We will use a two-group randomized controlled pretest-posttest design and collect data at baseline (T1) and 4 months later (T2) among 50 patients completing initial treatment for localized prostate cancer and their partners. First, we will assess the feasibility of ESCP by recruitment, enrollment, and retention rates; program satisfaction with the ESCP; and perceived ease of use of the ESCP. To achieve the secondary aim, we will compare the ESCP users with the standardized SCP users and assess their primary outcomes of QOL (overall, physical, emotional, and social QOL); secondary outcomes (reduction in negative appraisals and improvement in self-efficacy, social support, and health behaviors to manage symptoms); and number of visits to posttreatment care services between T1 and T2. We will assess the primary and secondary outcomes using measurements with sound psychometrical properties. We will use a qualitative and quantitative mixed methods approach to achieve the research aims. RESULTS: This project is ongoing and will be completed by the end of 2018. CONCLUSIONS: The results from this study will help design a definitive randomized trial to test the efficacy of the ESCPs, a potentially scalable program, to enhance supportive care for prostate cancer patients and their families.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".