ICU nurses’ perception, knowledge, and barriers on delirium assessment
Bibliographic record
Abstract
Background and objective: Despite the availability of assessment tools for identifying and managing delirium in clinical settings, most cases remain undiagnosed, which demands the importance of conducting educational sessions. There is a necessity to understand why ICU nurses are unable to assess delirium in ICU patients, and there is a need to establish the best practice to promote patient safety. The descriptive cross-sectional research design aimed to assess the ICU nurses’ perception, knowledge, and Perceived barriers to delirium assessment, assess the association between qualifications and previous education regarding delirium to their knowledge and perception, and evaluate the association between ICU nurses' experience and total scores of knowledge and perception.Methods: A total 105 ICU nurses were selected from one of the tertiary hospitals in the Sultanate of Oman. Socio-demographics about ICU nurses’ knowledge, perception of delirium, and barriers to proper delirium assessment were collected by using an online self-administrative survey after obtaining consent. Data were statistically analyzed for central tendencies and level of dispersion (mean, range, and standard deviation).Results: The result of this study illustrated that 60.2% of ICU staff nurses have a moderate perception of the importance of delirium assessment in ICU. The majority of the nurses are females who held bachelor’s degrees with experience of six to ten years in the critical care field.Conclusions: The finding in this study illustrated that most of the staff nurses have previous training regarding delirium. However, there are gaps in delirium assessment, perception, and knowledge in ICU. Therefore, appropriate education is required to increase delirium identification, skill, and knowledge.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".