Treatment Regret, Mental and Physical Health Indicators of Psychosocial Well-Being among Prostate Cancer Survivors
Bibliographic record
Abstract
Prostate cancer (PCa) patients and survivors are at high risk of mental health illness. Here, we examined the contribution of treatment regret, mental and physical health indicators to the social/family, emotional, functional and spiritual well-being of PCa survivors. The study assessed 367 men with a history of PCa residing in the Maritimes Canada who were surveyed between 2017 and 2021. The outcomes were social/family, emotional, functional and spiritual well-being (FACT-P,FACIT-Sp). Predictor variables included urinary, bowel and sexual function (UCLA-PCI), physical and mental health (SF-12), and treatment regret. Logistic regression analyses were controlled for age, income, and survivorship time. Poor social/family, emotional, functional and spiritual well-being was identified among 54.4%, 26.5%, 49.9% and 63.8% of the men in the sample. Men who reported treatment regret had 3.62, 5.58, or 4.63 higher odds of poor social/family, emotional, and functional well-being, respectively. Men with low household income had 3.77 times higher odds for poor social/well-being. Good mental health was a protective factor for poor social/family, emotional, functional, or spiritual well-being. Better physical and sexual health were protective factors for poor functional well-being. Seeking to promote PCa patients' autonomy in treatment decisions and recognizing this process' vulnerability in health care contexts is warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".