Nurses’ information exchange during older patient transfer: prevalence and associations with patient and transfer characteristics
Bibliographic record
Abstract
INTRODUCTION: To ensure continuity of care, it is important to effectively communicate the health status of older patients who are transferred between health care organizations. The objectives of this study were to: (1) evaluate the prevalence of nursing transfer documents, and (2) identify patient and transfer characteristics associated with the presence of nursing transfer documents for older patients transferred from home care to hospital and back to home care again after hospitalization. METHODS: Nursing documents were reviewed from a total of 102 records of older inpatients admitted from home care to medical wards at a local hospital in central Norway and later discharged home. Frequencies were used to describe patient and transfer characteristics, and the prevalence of transfer documents. Pearson's χ(2) test and logistic regression were used to identify possible associations between patient and transfer characteristics and the presence of nursing transfer documents. RESULTS: While nursing admission notes were present in 1% of the patient transfers from home care to the hospital, 69% of patient discharges from the hospital to home care were accompanied by nursing discharge notes. Patient and transfer characteristics associated with the presence of a nursing discharge note were age, gender, medical department facility, and length of hospital stay. CONCLUSIONS: The low prevalence of nursing transfer documents constitutes a challenge to the continuity of care for hospitalized home care patients. Patient and transfer characteristics may impact the nurses' propensity to exchange patient information. These findings emphasize the need for nurses and managers to improve the exchange of written information. While nurses must strive to transfer accurate patient information at the right place and at the right time, the managers must facilitate this by providing appropriate guidelines and standards, as well as adequate personnel and resources.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".