A survey of nurses' perceptions of the intensive care delirium screening checklist.
Bibliographic record
Abstract
OBJECTIVES: Delirium in critically ill patients is common and is associated with increased morbidity and mortality. Routine delirium screening is recommended by the Society of Critical Care Medicine. The Intensive Care Delirium Screening Checklist (ICDSC) is one validated and commonly-used tool, but little is known about nurses'perceptions of using the ICDSC, and of barriers to delirium assessment and treatment. DESIGN: A survey was administered to 189 critical care-trained nurses working on four oncology inpatient units, where the ICDSC has been used for greater than five years. RESULTS: Eighty-four nurses (44%) responded to the survey. Respondents indicated that they had knowledge of delirium, confidence in the ICDSC, and that the ICDSC was useful. Respondents perceived that physicians did not value the ICDSC results. Similar to prior nurse surveys for other delirium screening tools, physicians were the most frequently identified barrier to both delirium assessment and treatment, with other frequent barriers being lack of time, feedback on performance, and knowledge of delirium. CONCLUSIONS: The ICDSC is viewed favourably by nurses with experience using the tool. Future delirium screening programs should encourage physician engagement early in the planning process to help address perceived barriers to delirium assessment and treatment.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".