Avoidability of hospitalisations at the end of life; a model for community nurses and GPs.
Bibliographic record
Abstract
Background and introduction \nAlthough many patients prefer to stay and die at home at the end of life, many are hospitalised. For hospitalised patients with a short life expectancy, it has been estimated that a quarter of hospitalisations could have been avoided. Little is known about how to avoid hospitalisations for patients living at home. \n\nAim\nThe aim of the study was to develop a conceptual model for community nurses and general practitioners (GPs) of strategies for avoiding hospitalisation at the end of life for patients living at home. The research question was how hospitalisation at the end of life can be avoided, from the perspective of community nurses, family carers and GPs? \n\nMaterials and methods\nA qualitative design with in-depth interviews was used. Taking 30 cases of patients who died non-suddenly 15 community nurses, 18 family carers and 26 GPs were interviewed in depth. Of the 30 patients, 20 were hospitalised and 10 were not hospitalised in the last 3 months of life. The interview transcripts were analyzed using thematic analysis and a conceptual model was constructed based on the resulting themes. \n\nResults\nWe developed a model with five key strategies that could help avoid hospitalisation at the end of life. The key strategies were: \n1) marking the approach of death, and shifting the mindset; \n2) being able to provide acute treatment and care at home; \n3) anticipatory discussions and interventions to deal with expected severe problems; \n4) guiding and monitoring the patient and family in a holistic way through the illness trajectory; \n5) continuity of treatment and care at home. \nIf these five key strategies are followed in an interrelated way, this could help avoid hospitalisations, according to community nurses, family carers and GPs.\n\nConclusion\nThe merit of the model is that it offers insight in providing palliative care at home by community nurses and GPs and helps them to avoid hospitalisation at the end of life. It is recommended that for all patients residing at home community nurses and GPs work together as a team from the moment that it is marked that death is approaching up to the end of life.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".