Examination of differences in quality of life and use of support services in urban versus rural breast cancer patients.
Bibliographic record
Abstract
98 Background: Many patients travel great distances, both for testing and for medical care. Previous studies have indicated that there can be significantly poorer quality of life in urban cancer patients. The purpose of this study was to determine any differences in support service use and quality of life in rural versus urban patients in Saskatchewan. Methods: This study is a survey based qualitative and quantitative analysis of women with breast cancer receiving treatment at the Saskatoon Cancer Center (SCC) in Saskatchewan, Canada. Surveys included demographic information, support service utilization and suggestions, as well as the WHO-QOL-bref quality of life assessment. Surveys were collected from 51 women with an age range of 43 to 87 years with a median age of 63. Participants travelled a mean distance of 99.2km to treatment center (range 1.9 – 307.6 km, SD 101 km) with 49% women travelling greater than 100 km (rural group) and 51% travelling less than 100 km (urban group). Results: Only 50% of urban participants and 56% of rural participants reported using support services at the SCC though this was found to be statistically non-significant by Fisher Exact Testing (p= 0.781). The support services reported to be most useful were nursing phone assessments, medication information, pharmacist support and mailing of medications. Further support services that participants would like to have available include: online support groups and blogs, local support groups, physician visits to rural communities and patient liaisons to help navigate the system. Overall, urban participants rated their quality of life on average at 3.96 with rural participants reporting 3.84 on a five-point scale, though non-significant by t-test (p=0.617). The WHO-QOL-bref study examines quality of life in 4 domains: physical health, psychological wellbeing, social and environment. Urban participants scored 68%, 72%, 76% and 77% in the 4 domains respectively while rural participants scored 61%, 73%, 79% and 78% in the 4 domains respectively. Conclusions: Our study did not find any significant difference between urban and rural patients in reported quality of life or support service use.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".