Cancer Survivors Living in Rural Settings: A Qualitative Exploration of Concerns, Positive Experiences and Suggestions for Improvements in Survivorship Care
Bibliographic record
Abstract
In Canada, the number of cancer survivors continues to increase. It is important to understand what continues to present difficulties after the completion of treatment from their perspectives. Various factors may present barriers to accessing help for the challenges they experience following treatment. Living rurally may be one such factor. This study was undertaken to explore the major challenges, positive experiences and suggestions for improvement in survivorship care from rural-dwelling Canadian cancer survivors one to three years following treatment. A qualitative descriptive analysis was conducted on written responses to open-ended questions from a national cross-sectional survey. A total of 4646 individuals living in rural areas responded to the survey. Fifty percent (2327) were male, and 2296 (49.4%) were female; 69 respondents were 18 to 29 years (1.5%); 1638 (35.3%) were 30 to 64 years; and 2926 (63.0%) were 65 years or older. The most frequently identified major challenges (n = 5448) were reduced physical capacity and the effects of treatment. Positive experiences included family and friend support and positive self-care practices. The suggestions for improvements focused on the need for better communication and information about self-care, side effect management, and programs and services, with more programs available locally for practical and emotional support.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".