Patient, family and provider experiences with transfers from intensive care unit to hospital ward: a multicentre qualitative study
Bibliographic record
Abstract
BACKGROUND: Transfer of patient care from an intensive care unit (ICU) to a hospital ward is often challenging, high risk and inefficient. We assessed patient and provider perspectives on barriers and facilitators to high-quality transfers and recommendations to improve the transfer process. METHODS: We conducted semistructured interviews of participants from a multicentre prospective cohort study of ICU transfers conducted at 10 hospitals across Canada. We purposively sampled 1 patient, 1 family member of a patient, 1 ICU provider, and 1 ward provider at each of the 8 English-speaking sites. Qualitative content analysis was used to derive themes, subthemes and recommendations. RESULTS: The 35 participants described 3 interrelated, overarching themes perceived as barriers or facilitators to high-quality patient transfers: resource availability, communication and institutional culture. Common recommendations suggested to improve ICU transfers included implementing standardized communication tools that streamline provider-provider and provider-patient communication, using multimodal communication to facilitate timely, accurate, durable and mutually reinforcing information transfer; and developing procedures to manage delays in transfer to ensure continuity of care for patients in the ICU waiting for a hospital ward bed. INTERPRETATION: Patient and provider perspectives attribute breakdown of ICU-to-ward transfers of care to resource availability, communication and institutional culture. Patients and providers recommend standardized, multimodal communication and transfer procedures to improve quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".