Jordanian Intensive Care Unit Nurses’ Knowledge of Delirium Recognition
Bibliographic record
Abstract
INTRODUCTION: Delirium is a clinical syndrome that negatively affects the outcomes of the intensive care units patients if undetected early and treated well. However, this condition remains under recognized and poorly managed by health care providers including nurses. The objective of this study was to check nurses' knowledge level about delirium recognition. MATERIALS & METHODS: This was a cross-sectional study on 176 intensive care units nurses working in four major hospitals in Amman, Jordan. Data were collected using a self-reported likert scale questionnaire. RESULTS: Nurses lack the basic knowledge about delirium recognition; the mean was (52.65 ± 4.99). Older nurses (aged 31 years and above) have significantly higher levels of knowledge regarding delirium recognition compared to younger nurses (mean ± SD, 56.02 ± 18.1 vs. 49.28 ± 12.65, P < .005). Moreover, nurses with longer experience in intensive care units were more knowledgeable about delirium recognition (r=0.73, p <.001).Graduate studies have a positive effect on the knowledge level. Nurses holding master degree have significantly higher levels of knowledge regarding delirium recognition compared to those with Baccalaureate degree; (mean ± SD, 60.28 ± 16.86 vs. 53.0 ± 12.18, P < .005). CONCLUSION: Delirium is a widespread disorder in the intensive care units. Jordanian nurses lack the basic knowledge regarding essential characteristics of delirium and its recognition. Education of nurses in all care settings is vital and necessary.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".