A Qualitative Study of Emergency Department Delirium Prevention Initiatives
Bibliographic record
Abstract
Background: Delirium is a serious but preventable syndrome of acute brain failure. It affects 15% of patients presenting to emergency care and up to half of hospitalized patients. The emergency department (ED) often represents the entry point for hospital care for older adults and as such is an important site for delirium prevention. Objective: We sought to characterize delirium prevention initiatives in EDs in the United States and Canada. Methods: We conducted qualitative interviews with 16 ED administrators representing 14 EDs with delirium prevention initiatives. We used a combined deductive-inductive approach to code responses about involved staff, target patient population, and delirium prevention activities. Results: ED delirium prevention initiatives were largely driven by bedside nurses and occurred on an ad hoc basis, rather than systematically. Due to resource limitations, three EDs targeted older adults with high-risk conditions for delirium, rather than all patients age 65 and over. The most common delirium prevention interventions were offering assistive sensory devices (hearing amplifiers, reading glasses), having a toileting protocol, and offering patients food and drink. Conclusions: As minimal evidence exists about effective ED delirium prevention practices, low-cost and low-risk activities outlined by study participants are reasonable to use to improve patient experience and staff satisfaction.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".