Pre-COVID-19 knowledge, attitude and practice among nurses towards infection prevention and control in Bangladesh: A hospital-based cross-sectional survey
Bibliographic record
Abstract
INTRODUCTION: Hospital-acquired infections endanger millions of lives around the world, and nurses play a vital role in the prevention of these infections. Knowledge of infection prevention and control (IPC) best practices among nurses is a prerequisite to maintaining standard precautions for the safety of patients. AIM: The study aims to assess knowledge, attitudes, and practices (KAP) towards IPC including associated factors among the nurses of a tertiary care hospital in Bangladesh. METHODS: We conducted this hospital-based cross-sectional study from October 2017 to June 2018 at Dhaka Medical College Hospital among 300 nurses working in all departments. We calculated three KAP scores for each participant reflecting their current state of knowledge and compliance towards IPC measures. Descriptive, bivariate and multivariable analyses were conducted to determine KAP scores among nurses and their associated factors. RESULTS: Average scores for knowledge, attitudes, and practices were 18.6, 5.4, and 15.5 (out of 26, 7, and 24), respectively. The study revealed that the majority (85.2%) of the nurses had a good to moderate level of knowledge, half (51%) of them showed positive attitudes, and only one fifth (17.1%) of the nurses displayed good practices in IPC. The respondents' age, education, monthly income and years of experience were found to have statistical associations with having moderate to adequate level of KAP scores. Aged and experienced nurses were found more likely to have poor knowledge and unfavorable attitude toward IPC practices. CONCLUSION: The majority of nurses had good IPC knowledge, but their practices did not reflect that knowledge. In particular, nurses needed to improve the proper IPC practice for better patient care and to protect themselves. Regular IPC training and practice monitoring can enhance the IPC practice among nurses.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".