Gaining a better understanding of the needs of rural cancer patients requiring in-home palliative and end-of-life care and nursing care and services
Bibliographic record
Abstract
Issue: Access to in-home palliative and end-of-life care (PELC), qualified professionals, and high-quality nursing care and services in rural areas is limited and unequal, thus leading to an increase in unmet needs across the care trajectory of cancer patients. Objectives and methodology: A qualitative descriptive study was carried out to gain a better understanding of the needs of rural cancer patients receiving in-home PELC and to describe the nursing care and services available to them. Results: Five rural cancer patients requiring PELC reported a variety of needs, especially those arising from limited information resources and multiple time- and energy-consuming back-and-forth trips to urban centres. Seven nurses who provide in-home care and services to rural inhabitants outlined the challenges they face in addressing these needs. These are related primarily to the long distances they are called upon to travel, the limited number of specialized professional resources available, transfers to emergency departments, the dearth of PELC training and the lack of a dedicated PELC team. Conclusion: These findings helped gain a better understanding of the specific needs of rural cancer patients requiring in-home PELC, as well as the challenges that nurses must confront to help their patients remain in their own homes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".