An Assessment of Handover Culture and Preferred Information in the Transitions of Care of Elderly Patients
Bibliographic record
Abstract
Introduction Transitions of care for elderly patients in long term care (LTC) to the emergency department (ED) is fraught with communication challenges. Information preferred during these transitions has not been agreed upon. We sought to understand our local handover culture and identify what information is preferred in the transitions of care of these patients. Methods We performed a cross-sectional electronic survey that was distributed to 1470 healthcare providers (HCPs) and 82 patient and family advocates (PFAs) in two Canadian cities. The HCP group consisted of physicians and nurses in ED and LTC settings as well as paramedics. The survey was open for a period of one month with formal reminders sent weekly. Results A total of 12.9% of HCPs and 26.8% of PFAs responded to the survey. Only 41.3% of HCP respondents were aware of existing handover protocols and 83.2% indicated a desire for a single page handover form. HCPs identified concerns over handover culture surrounding workplace inefficiencies and increased demands to their time. Several preferred items of information in the transitions of care for the institutionalized elderly patient were also identified across both HCP and PFA groups. Conclusions Our study identified a need for improved local handover culture in transitions of care for the institutionalized elderly patient. We also identified the preferred elements of information during bilateral communication between LTC and the ED. Our results will be used to design a patient-centred handover form for future use in this population.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".