How Nurses Can Support Families Where a Person with Serious Illness Is Living at Home: A Scoping Review
Bibliographic record
Abstract
More tasks and responsibilities have been transferred to the caregivers of a seriously ill person living at home. The caregivers risk getting overburdened and sick themselves and therefore cannot help with sufficient support. Limited knowledge exists about what applies in a private home, and there is a general lack of knowledge about how healthcare professionals can support the caregivers. In this review, we investigated how nurses can support caregivers where a seriously ill person is living at home. The design was a scoping review. Literature published from 2007 to 2022 was searched in four databases: PubMed, CINAHL, Embase, and PsycINFO. Quality assessment was made by Hawkers model, and n = 18 studies from Northern Europe, the USA, and Canada were included. The methods used were 11 qualitative, 5 quantitative, and 2 mixed-method studies, including 971 caregivers, 217 patients, and 10 healthcare professionals. Multidisease, cancer, apoplexy, and recipients of palliative care were the main focus, but other diseases were also represented. The nurse’s task is to ensure the presence of a number of prerequisites for the care process to be successful and to be aware that caregivers may find themselves in a number of dilemmas and to try to remedy these dilemmas. If possible, the nurse should facilitate that the care burden is shared between several members of the family. Care must be performed according to the principles of family nursing, meaning that all relevant members of the family, appointed by the patient, are included, and collaboration is established. Future research and practice should focus on the nurses, the framework home care nurses work under, and how they can practice family nursing, so that family nursing becomes a natural part of primary care.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".