Improving the process of care setting transitions for adult palliative patients: recommendations for nurse practitioner practice
Bibliographic record
Abstract
The number of transitions between care settings for palliative patients increase as they approach death. In Canada, 40% of palliative patients experienced one care setting transition prior to death, 6.3% experienced five or more transitions, and 47% made at least one care setting transition in the last four weeks of life. Often, palliative patients are transferred between care settings in order to receive the care necessary to improve their quality of life. Many times these transfers lead to patient and caregiver anxiety and dissatisfaction, medication errors, and ultimately a decrease in the quality of care. The aim of this project is to answer the following question: How can nurse practitioners improve care setting transition processes for adult palliative patients in the context of primary care in British Columbia? An integrated review was undertaken and an extensive literature search was conducted by way of electronic databases, journals, reference lists, and guidelines. Results are groups into three categories or levels: system, clinician, and patient. Within these categories, the key findings in this review include improving communication, continuity of care, and multidisciplinary communication, effective medication reconciliation, adequate health information technology and improving patient and caregiver education and empowerment. Recommendations for nurse practitioners as primary care practitioner are presented in the areas of practice, education, and future research considerations. --Leaf ii.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".