Caregivers’ burden of care during emergency department care transitions among older adults: a mixed methods cohort study
Bibliographic record
Abstract
Abstract Objective Improving care transitions for older adults can reduce emergency department (ED) revisits, and the strain placed upon caregivers. We analyzed whether caregivers felt a change in burden following a care transition, and what may be improved to reduce it. Methods This mixed-methods observational study nested within LEARNING WISDOM included caregivers of older patients who experienced an ED care transition. Burden was collected with the brief Zarit Burden Interview (ZBI-12), and caregivers also commented on the care transition. A qualitative coding scheme of patient care transitions was created to reflect themes important to caregivers. Comments were randomly analyzed until saturation and themes were extracted from the data. We followed both the SRQR and STROBE checklists. Results Comments from 581 caregivers (mean age (SD) 64.5 (12.3), 68% women) caring for patients (mean age (SD) 77.2 (7.54), 48% women) were analyzed. Caregivers overwhelmingly reported dissatisfaction and unmet service expectations, particularly with home care and domestic help. Communication and follow-up from the ED emerged as an area for improvement. Caregivers who reported an increased level of burden following a patient’s care transition had significantly higher ZBI scores than caregivers who self-reported stable burden levels. Conclusion Caregivers with increasing, stable, and improved levels of subjective burden all reported areas for improvement in the care transition process. Themes centering on the capacity to live at home and inadequate communication were most frequently mentioned and may represent serious challenges to caregivers. Addressing these challenges could improve both caregiver burden and ED care transitions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".