A systematic review of the feasibility, acceptability, and efficacy of online supportive care interventions targeting men with a history of prostate cancer
Bibliographic record
Abstract
PURPOSE: To examine the feasibility, acceptability, and efficacy of online supportive care interventions targeting prostate cancer survivors (PCS). METHODS: Studies were identified through structured searches of PubMed, Embase and PsycINFO databases, and bibliographic review. Inclusion criteria were (1) examined feasibility, acceptability, or efficacy of an online intervention designed to improve supportive care outcomes for PCS; (2) presented outcome data collected from PCS separately (if mixed cancer); and (3) evaluated efficacy outcomes using randomized controlled trial (RCT) design. RESULTS: Sixteen studies met inclusion criteria; ten were classified as RCTs. Overall, 2446 men (average age 64 years) were included. Studies reported on the following outcomes: feasibility and acceptability of an online intervention (e.g., patient support, online medical record/follow-ups, or decision aids); reducing decisional conflict/distress; improving cancer-related distress and health-related quality of life; and satisfaction with cancer care. CONCLUSION: We found good preliminary evidence for online supportive care among PCS, but little high level evidence. Generally, the samples were small and unrepresentative. Further, inadequate acceptability measures made it difficult to determine actual PCS acceptability and satisfaction, and lack of control groups precluded strong conclusions regarding efficacy. Translation also appears minimal; few interventions are still publicly available. Larger trials with appropriate control groups and greater emphasis on translation of effective interventions is recommended. IMPLICATIONS FOR CANCER SURVIVORS: Prostate cancer survivors have a variety of unmet supportive care needs. Using online delivery to improve the reach of high-quality supportive care programs could have a positive impact on health-related quality of life among PCS.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".