Improving transitions between nursing homes and emergency departments: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Nursing home residents with acute illnesses have complex healthcare needs that often require transitions across multiple organizations. This study explores the experiences of diverse healthcare professionals from organizations involved in transitions between nursing homes and emergency departments and identifies conditions necessary to improve this transitional process. METHODS: Eighteen qualitative interviews with healthcare professionals were conducted, and data were analyzed using thematic analysis based on the work of Braun & Clarke. This paper adheres to the Standards for Reporting Qualitative Research (SRQR) standards. RESULTS: Three themes were identified: (1) Inclusive and supportive engagement, emphasizing the importance of involving care recipients while providing support; (2) Operational readiness, indicating the structures and supports needed to ensure that staff can effectively respond to unexpected events; and (3) Cross-organizational collaboration, pinpointing that streamlined communication strategies across healthcare organizations facilitate a shared decision-making process among healthcare professionals and ensure that essential patient information is readily accessible at each transitional step. CONCLUSIONS: A comprehensive approach addressing these individual, operational, and systemic factors can enhance transitional care between nursing homes and emergency departments.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".