Rural Women's Perspectives on Cancer Care in Southern Saskatchewan
Bibliographic record
Abstract
One in two Canadians will be diagnosed with cancer in their lifetime. A cancer diagnosis requires supportive care during diagnosis, treatment, follow up and palliative care. Those living in rural areas in Canada experience additional burdens in meeting supportive care needs. The purpose of this study was to explore the experiences of women who had been diagnosed with cancer living in a rural area, related to met and unmet supportive care needs. The intent is to give a voice to rural women with cancer and to inform oncology programming that is specific to rural residents. The study objectives were to understand the benefits and burdens to meeting supportive care needs of women living in rural areas with cancer and to identify ways that the rural healthcare system can meet these needs. The qualitative method, Interpretive Description, was used for this study. Data was collected by interviews with rural women who had been diagnosed with cancer within the last five years or who were still undergoing treatment. Data analysis was done concurrently with data collection, which is congruent with Interpretive Description. The findings of this study center around four themes and 14 sub-themes. The themes were “Feelings – From normal life to chaos”; ”Self-efficacy and resiliency – Get up and get going ‘cause there’s things to do”; “Timing – And then the whole month was just waiting”; and “Access – For the most part you just drive”. Generally, the rural women were often found to be in deficit positions with respect to select service providers, health information, and logistical issues, but they remained thankful, positive, and engaged in their care. Recommendations for this study included the use of social media and online services for support and informational needs, and the use of a nurse navigator who is specific to rural oncology, as well as recommendations for nursing practice, policy, and future research.
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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 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".