Unmet supportive care needs in prostate cancer survivors with advanced disease: A mixed-methods exploration
Bibliographic record
Abstract
Purpose: Men with advanced prostate cancer experience a wide range of side effects from the cancer and its therapies, which have a negative effect on their quality of life (QOL). Few studies have evaluated supportive care needs in these individuals. The purpose of this study was to conduct a holistic supportive care needs assessment among these survivors guided by the Supportive Care Framework for Cancer Care. Methods: Using a convergent parallel mixed-methods approach, prostate cancer survivors with advanced disease (n = 188) completed a cross-sectional survey. A subset of these survivors (n = 20) participated in an interview to further explore their experience of unmet needs. Results: Survivors reported unmet supportive care needs in every domain of the framework. Up to 95.2% of the survivors had at least one unmet need, with a mean of 14.9 (range: 0-42). Several areas of convergence among the quantitative and qualitative data (fatigue, sexual dysfunction, practical, and emotional/psychological domains), as well as divergence (informational and spiritual domains, depression, urinary dysfunction) were found through the integration process. Conclusions: This study confirms that prostate cancer survivors with advanced disease experience high rates of unmet supportive care needs. The findings also highlight the diversity of those unmet needs. These results may assist with future development of patient-centered supportive care interventions that better meet the specific needs of this vulnerable group of cancer survivors.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".