NURSING PRACTICE AND OPTIMAL DELIRIUM CARE AMONG OLDER ADULTS IN ACUTE CARE SETTINGS
Bibliographic record
Abstract
Abstract Studies have emphasized the limited availability of data regarding the current practices of nurses in delirium care for patients in acute care settings. Similarly, there is a lack of information regarding the obstacles and facilitators in providing optimal care for the prevention, detection, and management of delirium. Aim To describe nurses’ practices about delirium care in acute care patients and their perceptions about barriers and facilitators regarding optimal care. Method: A two phase, multi method design was used. The quantitative phase utilized a self-reported survey to assess nurses’ knowledge, practice, confidence, and collaboration regarding delirium care. The qualitative phase employed focus groups to complement and explore survey data in-depth. Recruitment took place on nine acute surgical and medical units across two university-affiliated hospitals in Canada, involving nurses in direct patient care. Results 231 nurses reveal diverse insights into delirium care in acute settings and survey participants showed solid knowledge of delirium symptoms and outcomes, yet 23% did not identify the hypoactive form’s prevalence. While 81% reported receiving information on delirium screening, challenges included time constraints and misuse of detection tools. Qualitative themes highlighted communication’s vital role, challenges posed by delirium presentations, the significance of staff support, time constraints, the impact of experienced staff, the role of families, and the importance of additional resources for optimal delirium care. Conclusion Findings align with existing literature, emphasizing the multifaceted nature of delirium care and the need for tailored approaches, education, and collaborative strategies to improve overall care quality.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".