A Cross-Sectional Study Evaluating Potential Differences in the Need for Cancer Survivorship Care in Relation to Patients’ Socioeconomic Status
Bibliographic record
Abstract
BACKGROUND: There seems to be inequity within cancer survivorship care in primary care settings related to gender, shorter education, and early poor health, but there is uncertainty regarding the character of the needs in hospital and in primary care settings and whether there is inequity regarding meeting these needs. This study aims to describe potential differences in needs among patients in hospital and in primary care settings, and to assess the need for survivorship care and rehabilitation in patients with cancer in relation to socioeconomic status. METHODS: In a cross-sectional study including patients in hospital (n = 89) and primary care settings (n = 99), information from needs assessments was linked with population-based data on socioeconomic status via unique personal identification numbers. The association between socioeconomic status and stated needs was analyzed separately for patients in hospital and primary care settings, with adjustment for age and gender. RESULTS: A total of 90% patients expressed one or more needs in the physical area, 51% in the emotional area, and 40% in the practical area. Patients in primary care expressed more needs than patients in hospital. Men expressed more needs than women in primary care (adjusted odds ratio (OR) (95% confidence interval (CI)): 2.66 (1.04 - 6.79)). The results indicate that higher socioeconomic status is associated with fewer stated needs. CONCLUSIONS: This study suggests that the association between gender and stated needs may depend on healthcare setting and confirms that higher socioeconomic status in relation to civil status, educational level, income, and labor market status is associated with fewer stated needs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".