Feasibility and Acceptability of Social Prescribing for Cancer Survivors
Bibliographic record
Abstract
Following cancer treatment, individuals experience a range of physical, mental and social health difficulties that interfere with their ability to resume participation in pre-cancer activities. In Ireland, the National Cancer Strategy recommends community-based services to address post-treatment difficulties. Social prescribing is a community-based, non-medical service that links individuals with health-related activities and supports in their community. This study explored the feasibility and acceptability of social prescribing for cancer survivors. A mixed methods study was undertaken with individuals who had completed curative treatment for any cancer type. Recruitment was carried out in a national cancer centre. Quantitative outcomes included feasibility metrics (recruitment, intervention adherence and retention), the Frenchay Activities Index (FAI), the Hospital Depression and Anxiety Scale (HADS), the Multidimensional Assessment of Fatigue (MAF), and EORTC QLQ-C30. Qualitative interviews explored acceptability of social prescribing. Data were analysed using descriptive statistics (quantitative data) and content analysis (qualitative data). Out of 131 individuals identified as eligible to participate, 43 agreed to participate (32.8% recruitment) and 27 met a link worker and were connected to a local activity (62.7% adherence) and completed follow-up outcome measures (62.7% retention). Improvements were observed in all health-related outcomes and those interviewed identified the intervention as acceptable. Study participants attended a range of community-based activities as a result of link worker support. They also reported increased confidence, improved mental health and reduction in fatigue following attendance at community-based activities. The findings of this study indicate that social prescribing is a feasible and acceptable community-based intervention to improve the physical, mental and social health of individuals living with and beyond cancer. A pilot randomised trial is indicated to inform a definitive intervention trial.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".