Rural‐urban differences in distress, quality of life, and social support among Canadian young adult cancer survivors: A Young Adults with Cancer in Their Prime (YACPRIME) study
Bibliographic record
Abstract
PURPOSE: Geographic location plays a significant role in the health and wellbeing of those with cancer. This project explored the impact of rurality and social support on distress and quality of life in young adults (YAs) with cancer in Canada. METHODS: The current research analyzed data from the Young Adults with Cancer in Their Prime (YACPRIME) study. Participants completed measures of emotional distress (10-item Kessler Psychological Distress Scale), quality of life (12-item Short-form Health Survey), and social support (Medical Outcomes Study-Social Support Survey). Rural and urban-dwelling survivors were compared using MANOVAs. Bivariate analyses were conducted to explore associations between distress and social support. FINDINGS: Of the sample (N = 379), 26% identified their location as rural. Rural YA cancer survivors reported higher distress and worse physical health-related quality of life (HRQOL) than survivors from urban areas but similar levels of mental-health-related quality of life. Social support appeared to have a marginally greater effect on psychosocial outcomes for urban participants. All types of social support were associated with lower levels of distress. However, different types of social support were associated more strongly with distress depending on a participant's geography. CONCLUSIONS: Rural-dwelling YA cancer survivors experience significantly more distress and poorer physical HRQOL than urban-dwelling survivors. Different needs and preferences for social support may influence the psychological health of rural cancer survivors. Additional research is needed to determine how best to understand and address distress in rural YAs with cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".