An Emergency Department Delirium Screening and Management Initiative: The Development and Refinement of the SCREENED-ED Intervention
Bibliographic record
Abstract
The current article describes an intervention aimed at emergency department (ED) nurses and physicians that was designed to address the challenges of managing delirium in the ED environment. The intervention development process followed the Medical Research Council principles paired with a user-centered design perspective. Expert clinicians and nursing staff were involved in the development process. As a result, the SCREENED-ED intervention includes four major components: screening for delirium, informing providers, an acronym (ALTERED), and documentation in the electronic health record. The acronym “ALTERED” includes seven key elements of delirium management that were considered the most evidence-based, relevant, and practical for the ED. Nurses are at the frontline of delirium recognition and management and the SCREENED-ED intervention with the ALTERED acronym holds the potential to improve nursing care in this complex clinical setting. [ Journal of Gerontological Nursing, 47 (12), 13–17.]
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".