An Interactive E-learning Platform-Based Training to Improve Intensive Care Professionals' Knowledge Regarding Central Venous Catheter-Related Infections
Bibliographic record
Abstract
Introduction The presence of a central venous catheter (CVC) leads to a high risk for blood infections, which are associated with increases in morbidity, mortality, and costs. This study aims to assess intensive care unit (ICU) nurses' and physicians' knowledge regarding the Centers for Disease Control and Prevention (CDC) guidelines for preventing CVC-related infections before and after an interactive distance education delivered through the e-learning platform Teleprometheus. Materials and methods The study was conducted among 85 nurses and physicians in Nicosia's General Hospital Intensive Care Unit (NGH-ICU) and high dependency unit (HDU). A validated questionnaire was used to assess nurses' and physicians' knowledge. Results Prior to the online interactive distance education, the mean total knowledge score was x̄ = 4.8 (SD = 2.46), while after, the mean total knowledge score increased to x̄ = 8.9 (SD = 2.38) (p<0.001). ICU physicians had a higher mean total knowledge score (x̄ = 10.20) than ICU nurses (x̄ = 8.75) after the intervention. There was no correlation between years of experience in the ICU and the level of knowledge (r = 0.048). The interactive distance education was positively evaluated by the participants, through a questionnaire, specially designed for this study. Discussion The most important findings were that (a) the level of knowledge of the participants improved with a statistically significant difference after the completion of the e-course, (b) the level of knowledge of the participants, after the completion of the e-course, was much higher from other studies, (c) there was no correlation between the years of experience of ICU health professionals and their level of knowledge, and (d) the interactive distance e-course was positively evaluated and satisfied the participants. Conclusion The current study demonstrates that in high-intensity work environments, such as ICUs, adopting e-learning approaches seems more necessary than ever.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".