Building bridges between clinic and community: Supporting patients and caregivers living in rural and remote Canada.
Bibliographic record
Abstract
Advances in the detection, diagnosis, and treatment of cancer have paralleled significant developments in the understanding of tumour biology, pathophysiology, and genomics. In spite of this, cancer remains the leading cause of death in Canada, with an estimated two in five Canadians expected to be diagnosed with cancer and one in four Canadians expected to die of cancer in their lifetime. Although Canada has a publicly funded, universal healthcare system, profound inequities exist across the country. Such inequities are often due to a multitude of intersecting factors. The focus of this paper is to review the impact of rurality on cancer care. People residing in rural and remote regions are known to have reduced access to and availability of cancer care, from prevention through diagnosis, treatment, follow-up, and palliative care. Potential strategies to mitigate the challenges associated with rurality will be discussed, including an overview of the role that nurses can play in addressing the needs of patients in rural regions. Oncology nurses are well suited to help support patients, their loved ones, and healthcare colleagues in rural settings with a view to helping improve equity in access to care, quality of care, and outcomes of care for all Canadians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".