Examining bed-bath practices of critically ill patients
Bibliographic record
Abstract
Introduction : Daily bed-baths are usually provided for most critically ill patients to improve patient hygiene, promote comfort and improve health outcomes. Critically ill patients are at greater risk for skin colonization and infection with multidrug-resistant organisms. Therefore, it is important to provide critically ill patient with effective personal hygiene especially bed-bath as poor hygiene may increase the risk of infection. The decision for bed-bath depends on the judgment of the caring nurse. The aim of this work was to describe bed-bath practices in intensive care units. Methods : A descriptive design was used. Sixty intensive care unit nurses were involved. Tool: “Bed-bath practices of critically ill patients’ assessment sheet” was used to collect data. Results : More than three quarters of nurses, 79% had improper bed-bath practices. The gap for safe bed-bath practices between nurses’ current bed-bath practices and the bed-bath evidence-based recommendations is wide (83%). Nurses’ self-reported reasons that hinder safe bed-bath practices were financial resources, followed by lack of equipment, no policy, lack of knowledge, and workload. Conclusions : Although, bed-bath is a routine nursing procedure, critical care nurses in the current study had poor skills and knowledge regarding it. The factors affecting bed-bath practice are financial resources, lack of equipment, no policy, lack of knowledge and workload. In-service training program should be conducted for nurses regarding putting priority of nursing care, determining timing and frequency for the bed-bath.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".