Exploring the Competency of the Jordanian Intensive Care Nurses towards Endotracheal Tube and Oral Care Practices for Mechanically Ventilated Patients: An Observational Study
Bibliographic record
Abstract
Oral care is an important feature of nursing; it is known that oropharynx is considered the main reservoir of bacterial colonization, so the removal of oral infection is a major duty of all health care providers, particularly nurses. We performed this study to explore endotracheal tube and oral care practices for mechanically ventilated patients of Jordanian intensive care nurses, and to study Jordanian intensive care nurses' practices during, prior to, and post endotracheal tube and oral care for mechanically ventilated patients. Endotracheal tube and oral care of Jordanian intensive care nurses for mechanically ventilated patients was compared with recommendations for endotracheal tube and oral care of American Association of Critical Care Nurses and guidelines of Centers for Disease Control and Prevention. Non- participant structured observational design was conducted using a 24 -item structured observational schedule. The findings show that nurses different in their oral care practices; did not follow American Association of Critical Care Nurses recommendations; and therefore delivered lower-quality oral care than predictable. Important inconsistencies were observed in the nurses' hyperoxygenation, respiratory assessment techniques and infection control practices.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".