Factors Related to Nurses Compliance in Hand Hygiene Businesses in Kudungga District of East Kutai
Bibliographic record
Abstract
<p>In an effort to reduce the incidence of HAIs, hand hygiene is very important because nurses always interact directly with the patient and the patient's environment. The objective is to analyze the factors related to the compliance of nurses in the implementation of hand hygiene in the hospital room of RSUD Kudungga district of Eastern Kutai in 2023. This research method is a type of quantitative researcher with analytical studies and with cross sectional design. In this study, the sampling technique used is Non-probobality sampler with purposive sampler. There are several factors in the implementation of hand-hygiene where these factors are facility factors, reward and punishment, compliance, attitude, knowledge and role of the PPI Team influence on the execution of hand hygiene to be maximum especially in Mutiara RSUD Kudungga district east quarter in 2023.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".