Unmet Supportive Care Needs in Cancer Survivors in Spain: A Multicentre Cross-Sectional Study on Prevalence and Sociodemographic and Disease-Related Risk Factors
Bibliographic record
Abstract
OBJECTIVE: This multicentre study investigates unmet supportive care needs (SCNs) among cancer survivors in Spain and analyses sociodemographic and cancer-related risk factors. METHODS: A cross-sectional design was used with 1862 cancer survivors aged 18-92 years who had completed primary treatment with curative intent and were disease-free. Participants responded to the Cancer Survivors' Unmet Needs (CaSUN) questionnaire. Descriptive and multivariate analyses explored SCNs in the total sample and subgroups, as well as differences according to sociodemographic and cancer-related variables. RESULTS: At least 20% of participants reported 18 needs out of a total of 35 identified by the CaSUN questionnaire. One-third to half reported needs in the comprehensive care and information domain. Risk factors for reporting more needs included younger age; female sex; not having a partner; being on sick leave or unemployed; having a diagnosis of haematological, breast or gynaecological cancer; receiving systemic treatment (chemotherapy and/or hormone therapy); and being at an earlier stage of survival. CONCLUSIONS: The study highlights significant unmet care needs among cancer survivors in Spain and the urgency of improving management of the physical and psychosocial effects of cancer and its treatment. Special attention should be given to those at greatest risk through personalised and comprehensive care strategies integrated into survivorship programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".