653: PATIENT AND PATIENT-FAMILY ENGAGEMENT IN INTENSIVE CARE UNIT DISPOSITION: MIXED-METHODS STUDY
Bibliographic record
Abstract
Introduction: The transfer of ICU patients to lower acuity settings is a major transition in care. We evaluated how patient and patient-family engagement in ICU transfer/discharge can be improved. Methods: We conducted a mixed-methods study in ICUs in Regina General Hospital (Regina, Canada) from May-June 2023. The ICUs conduct daily multidisciplinary rounds and have a 24-hour family presence model. We recruited ICU survivors (and their family members) who were listed for transfer/discharge. Semi-structured interviews were conducted. Thematic analysis of audio transcripts was performed with NVivo. Results: Thirty-six patients were screened over a six-week period with 13 participants interviewed. Seven (54%) were patients and six (46%) were substitute-decision makers. Only two (15%) were related to the same patient. Participants had a mean age 49.4 years (SD 16.6). Six (46%) were female, six (46%) were male, and one (8%) preferred not to answer. Patients had a median Charlson score 3 (IQR 2-5) and SOFA 10 (IQR 6-11). In terms of engagement, 46% felt engaged with ICU transfer and 85% felt engaged with ICU care overall. Those who felt engaged reported feeling informed and consulted. Communication with nurses and conduct of daily rounds were the largest factors in engagement. Lack of discussion regarding transfer led to reports of feeling less engaged. Other factors included a lack of expectation to be informed/consulted due to the acuity of the patient, as well as the patient’s condition impairing them from having an active role in care. Suggestions to improve engagement included introducing oneself and talking through tasks when performing patient care, filling out whiteboards in patient rooms, checking for patient and patient-family understanding during rounds, informing patients and patient-families prior to transfer, and addressing expectations/concerns regarding changes in level of care. Conclusions: Patient and patient-family engagement in ICU transfer/discharge was driven by feeling informed and consulted about the decision to transfer/discharge. Barriers to engagement include poor communication with staff and discussion surrounding the transfer or transfer process. Addressing these processes may improve patient and patient-family engagement.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".