Predictors of prostate cancer survivors’ engagement in self-management behaviors
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer survivors experience a multitude of late treatment effects, resulting in greater unmet needs, elevated symptom burden, and reduced quality of life. Survivors can engage in appropriate self-management strategies post-treatment to help reduce the symptom burden. The objectives of this study were to: 1) survey the unmet needs of prostate cancer survivors using the validated Cancer Survivor Unmet Needs instrument; 2) explore predictors of high unmet needs; and 3) investigate prostate cancer survivors' willingness to engage in self-management behaviors. METHODS: Survivors were recruited from a prostate clinic and a cross-sectional survey design was employed. Inclusion criteria was having completed treatment two years prior. Descriptive statistics were used to summarize participant characteristics. Univariate and multivariate analyses were done to determine predictors of unmet needs and readiness to engage. RESULTS: A total of 206 survivors participated in the study, with a mean age of 71 years. Most participants were university/college-educated (n=123, 61%) and had an annual household income of ≥$99 999 (n=74, 38%). Participants reported erectile dysfunction (81%) and nocturia (81%) as the most frequently experienced symptoms with the greatest symptom severity χ̄=5.8 and χ̄=4.5, respectively). More accessible parking was the greatest unmet need in the quality-of-life domain (n=34/57, 60%). Overall, supportive care unmet needs were predicted by symptom severity on both univariate (p<0.001) and multivariate analyses (odds ratio [OR ] 1.81, 95% confidence interval [CI] 0.92-1.00, p<0.001). Readiness to engage in self-management was predicted by an income of <$49 000 (OR 3.99, 95% CI 1.71-9.35, p=0.0014). CONCLUSIONS: Income was the most significant predictor of readiness to engage in self-management. Consideration should be made to establishing no-cost and no-barrier education programs to educate survivors about how to engage in symptom self-management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".