Nurse-Physician Collaborative Partnership: a rural model for the chronically ill.
Bibliographic record
Abstract
INTRODUCTION: Accessibility and quality of primary health care services in rural areas are challenging issues, particularly for the elderly and those with chronic or complex medical conditions. The objective of the Nurse-Physician Collaborative Partnership was to implement and evaluate a collaborative partnership between homecare nurses and family physicians in the rural Trochu-Delburne-Elnora area of Alberta. METHODS: Overall, 37 patients were enrolled in a shared care plan, which included comprehensive biopsychosocial assessment, early intervention, health education and self-management. Patient and provider outcomes were assessed using quantitative and qualitative data collected at baseline, 6 months and 12 months. RESULTS: Results showed that patients made improvements in activities of daily living and robust cognitive status. In interviews, patients reported improvements in psychological well-being, knowledge of disease processes and confidence to manage health issues. Patients' use of acute health care services decreased, showing a 51% reduction in the number of days in hospital, a 32% reduction in emergency department visits and a 25% reduction in hospital admissions. Total acute service costs, excluding program costs, decreased by 40% from an average of $15,485 to $9,313 per person (p < or = 0.05). CONCLUSION: Based on these results, policy initiatives that incorporate the shared care model developed in this project may be considered. To our knowledge, this type of evaluation has not previously been conducted in a rural Canadian setting.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".