Facilitators and barriers to participation in lifestyle modification for men with prostate cancer: A scoping review
Bibliographic record
Abstract
PURPOSE: Diet and physical activity changes have been shown to improve quality of life and health outcomes for prostate cancer (PC) survivors; however, few survivors make lifestyle changes. We aimed to identify PC-specific facilitators and barriers to dietary and physical activity changes and participation in survivorship-based lifestyle management programmes. METHODS: A scoping review investigating facilitators and barriers of PC survivor's participation in lifestyle management programmes was conducted in June 2018. A total of 454 studies were identified, 45 studies were assessed in full, and 16 were included in the scoping review. RESULTS: Barriers to lifestyle change included perceived lack of evidence for lifestyle guidelines, treatment side effects, perception of change as unnecessary, time pressure and age. Facilitators for lifestyle change included advice from health professionals, support systems (family and peer), diagnosis as a time for change, lifestyle as a coping strategy to manage side effects and improve well-being. CONCLUSIONS: Health professionals, peers and family have a significant role in lifestyle management for PC survivors to facilitate engagement. Specific and clear messaging of the benefits of lifestyle management is warranted. Treatment-related side effects, time pressure, current health perception and age should be considered when developing lifestyle management programmes for PC 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".