Social Wellbeing in Cancer Survivorship: A Cross-Sectional Analysis of Self-Reported Relationship Closeness and Ambivalence from a Community Sample
Bibliographic record
Abstract
Improvements in early screening and treatment have contributed to the growth of the number of cancer survivors. Understanding and mitigating the adverse psychosocial, functional, and economic outcomes they experience is critical. Social wellbeing refers to the quality of the relationship with partners/spouses, children, or significant others. Close relationships contribute to quality of life and self-management; however, limited literature exists about social wellbeing during survivorship. This study examined positive and negative self-reported changes in a community sample of 505 cancer survivors. Fourteen items assessed changes in communication, closeness with partner/children, stability of the relationship, and caregiving burden. An exploratory factor analysis was conducted using a robust weighted least square procedure. Differences by sociodemographic and clinical characteristics were investigated. Respondents were mostly male, non-Hispanic white, and ≥4 years since diagnosis. Two factors, labeled Relationship Closeness and Ambivalence, emerged from the analysis. Women, younger survivors, individuals from minority groups, and those with lower income experienced greater negative changes in social wellbeing. Variations by treatment status, time since diagnosis, and institution were also reported. This contribution identifies groups of cancer survivors experiencing affected social wellbeing. Results emphasize the need to develop interventions sustaining the quality of interpersonal relationships to promote long-term outcomes.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".