Nurses’ actions for Covid-19 patients' transitioning from hospital to home: a scoping review
Bibliographic record
Abstract
Purpose To map nurses’ actions performed during the care transitions from hospital to home of Covid-19 patients. Design/methodology/approach A scoping review based on the Joanna Briggs Institute guidelines was carried out. We searched in seven databases: PubMed/MEDLINE, BDENF, LILACS, SciELO, Embase, Scopus, Web of Science and Google Scholar. A two-step screening process and data extraction was performed independently by two reviewers. The findings were summarized and analyzed using a content analysis technique. Findings Of the total 5,618 studies screened, 21 were included. The analysis revealed nurses’ actions before and after patient’ discharge, sometimes planned and developed with the interprofessional team. The nurses’ actions included to plan and support patients’ discharge, to adapt the care plan, to use screening tools and monitor patients’ clinical status and needs, to provide health orientation to patients and caregivers, home care and face-to-face visiting, to communicate with patients, caregivers and other health professionals with phone calls and virtual tools, to provide rehabilitation procedures, to make referrals and to orient patients and families to navigate in the health system. Practical implications The results provide a broader understanding of the actions taken and challenges faced by nurses to ensure a safe care transition for Covid-19 patients from hospital to home. The interprofessional integration to discharge planning and the clinical nursing leadership in post-discharge monitoring were highlighted. Originality/value The nurses’ actions for Covid-19 patients performed during care transitions focused on coordination and discharge planning tailored to the needs of patients and caregivers at the home setting. Nurses monitored patients, with an emphasis on providing guidance and checking clinical status using telehealth tools.
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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 | 0.025 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".