A Survey of Nurses' Perspectives on Delirium Screening in Older Adult Medical Inpatients With Limited English Proficiency
Bibliographic record
Abstract
The Confusion Assessment Method (CAM) is commonly used to detect delirium but its utility in patients with limited English proficiency (LEP) is not well-established. In the current study, internal medicine nurses at an acute care hospital in Canada were surveyed on the use of the CAM in older adults with LEP. Nurses' perspectives were explored with a focus on barriers to administration. Fifty participants were enrolled (response rate = 47.6%). Twenty-eight (56%) participants stated they could not confidently and accurately assess delirium in patients with LEP. Twenty-nine (58%) participants believed the CAM is not an effective delirium screening tool in the LEP population. Barriers to screening included: challenges with interpretation services, dependence on family members, and fear that the assessment itself may worsen confusion. Our study is the first to describe specific barriers to administering the CAM in patients with LEP. Strategies are required to address these barriers and optimize delirium screening for patients with LEP. [ Journal of Gerontological Nursing, 47 (4), 29–34.]
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".