Nurse-led navigation to provide early palliative care in rural areas: a pilot study
Bibliographic record
Abstract
BACKGROUND: Few services are available to support rural older adults living at home with advancing chronic illness. The objective of this project was to pilot a nurse-led navigation service to provide early palliative support for rural older adults and their families living at home with advancing chronic illness. METHODS: Twenty-five older adults and 11 family members living with advancing chronic illness received bi-weekly home visits by a nurse navigator over a 2-year period. Navigation services included symptom management, education, advance care planning, advocacy, mobilization of resources, and psychosocial support. The nurse navigator collected longitudinal data on older adult and family needs, and older adult quality of life and healthcare utilization. RESULTS: Satisfaction with the service was high. There was no attrition over the 2-year period except through death, and few cancelled visits, indicating a high degree of acceptability of the intervention. The navigator addressed complex, multi-faceted needs through connecting health, social, and informal community resources. Participants who indicated a preferred place of death were able to die in that preferred place (n = 7). Emergency room use by participants was minimal and largely unpreventable by the nurse navigator. Longitudinal health-related quality of life scores for many participants were poor, lending further support to the need for more focused attention to this upstream palliative population. CONCLUSIONS: Using a nurse navigator to facilitate early palliative care for rural older adults living with advanced chronic illness is a promising innovation for meeting the needs of this population. Further research is required to evaluate outcomes on a larger scale.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".