Supportive care interventions and quality of life in advanced disease prostate cancer survivors: An integrative review of the literature
Bibliographic record
Abstract
BACKGROUND: Supportive care interventions can improve quality of life and health outcomes of advanced prostate cancer survivors. Despite the high prevalence of unmet needs, supportive care for this population is sparse. METHODS: The databases PubMed, SCOPUS, CINAHL, and ProQuest were searched for relevant articles. Data were extracted, organized by thematic matrix, and categorized according to the seven domains of the Supportive Care Framework for Cancer Care. RESULTS: The search yielded 1678 articles, of which 18 were included in the review and critically appraised. Most studies were cross-sectional with small, non-diverse samples. Supportive care interventions reported for advanced prostate cancer survivors are limited with some positive trends. Most outcomes were symptom-focused and patient self-reported (e.g., anxiety, pain, self-efficacy) evaluated by questionnaires or interview. Interventions delivered in group format reported improvements in more outcomes. CONCLUSIONS: Additional supportive care intervention are needed for men with advanced prostate cancer. Because of their crucial position in caring for cancer patients, nurse scientists and clinicians must partner to research and develop patient-centered, culturally relevant supportive care interventions that improve this population's quality of life and health outcomes. Efforts must concentrate on sampling, domains of needs, theoretical framework, guidelines, and measurement instruments.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".