Detection of Acute Confusion in Taiwanese Elderly Individuals: Cultural Influences
Bibliographic record
Abstract
Prevention and early detection can optimize outcomes for older adults with acute confusion, but cultural misconceptions can interfere with proper treatment. This article explores how nurses can better meet the needs of older adults from all cultures. EXCERPT Based on research conducted in America and Europe, acute confusion (AC) is a common, reversible mental disorder of elderly individuals (Cole, Primeau, & Elie, 1998; Dai, Lou, Yip, & Huang, 2000; Inouye, 2000; Milisen et al., 2002). Research suggests that the prevalence of AC among elderly individuals is significantly associated with treatment settings. For example, the prevalence of AC among hospitalized elderly individuals ranges from 10% to 80% in the United States, Canada, and several European countries (Elie, Cole, Primeau, & Bellavance, 1998; Inouye, 2001), with surgical units having a higher rate than medical units. Prevalence estimates of AC among elderly residents in nursing homes ranges from 13.8% to 40.5% (Culp et al., 1997; Mentes, Culp, Maas, & Rantz, 1999).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".