Health-Promoting Behaviours following Primary Treatment for Cancer: A Rural–Urban Comparison from a Cross-Sectional Study
Bibliographic record
Abstract
Aim: To compare health-promoting behaviours among rural and urban residents following primary treatment for cancer. Methods: A cross-sectional survey collecting demographic variables and data pertaining to health-promoting behaviours, documented using the 52-item Health Promotion Lifestyle Profile II (HPLP-II) measure, which is categorised into six subscales: (1) health responsibility, (2) spiritual growth, (3) physical activity, (4) interpersonal relations, (5) nutrition, and (6) stress management. Residence was defined using the U.K. Office for National Statistics RUC 2011 Rural Urban Classifications. The Index of Multiple Deprivation (IMD) Decile was used to measure deprivation. Quantitative data were analysed using independent samples t-test and multiple linear regression. Qualitative data from open-ended questions were analysed thematically. Results: In total, 227 participants with a range of cancer types completed the questionnaire. Fifty-three percent were residents in urban areas and forty-five percent in rural areas. Rural participants scored significantly higher on health responsibility (p = 0.001), nutrition (p = 0.001), spiritual growth (p = 0.004), and interpersonal relationships (p = 0.001), as well as on the overall HPLP-II (p = 0.001). When controlling for deprivation, age, marital status, and education, rural–urban residence was a significant predictor of exhibiting health-promoting behaviours. A central theme from the qualitative data was the concept of “moving on” from cancer following treatment, by making adjustments to physical, social, psychological, spiritual, and emotional wellbeing. Conclusions: This research revealed, for the first time, differences in health-promoting behaviours among rural and urban U.K. populations who have completed primary cancer treatment. Rural residence can provide a positive environment for engaging with health-promoting behaviours following a cancer diagnosis and treatment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".