Objectives: The study sought to assess the impact of applying a learning strategy to improve the quality of sample collection during cervical screening by students from the Nursing Degree Program doing social service.
Methods: This was a longitudinal, quasi-experimental study with the participation of 23 interns from the Nursing Degree Program at a public university from San Luis Potosí, Mexico. The work assessed knowledge of practical skills in taking cervical cytology tests and the quality of samples before and after applying a learning strategy that included 10 h of theoretical training and 22 h of practices on themes related to sample collection in cervical screening.
Results: A statistically significant difference was obtained in improved knowledge (t = -12.8 p<0.001) and practical skills (t = -8.86 p<0.001) after the intervention. The increased percentage of suitable samples from 30.43% to 82.60% was attributed to the application of the learning strategy in the pre- and post-intervention phases (p<0.001).
Conclusions: Training is effective to improve knowledge and practical skills to collect samples in cervical screening, as well as the quality of the samples for their interpretation.
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http://dx.doi.org/10.17533/udea.iee.v35n3a10 | DOI Listing |
PLoS One
January 2025
Department of Computer Science, University of Jaén, Jaén, Spain.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Systems and Cybersecurity, University of Bisha, Bisha, KSA.
Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
Rwanda Ministry of Health, Kigali, Rwanda.
Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences.
View Article and Find Full Text PDFPlant Physiol
January 2025
Rothamsted Research, West Common, Harpenden, Al5 2JQ, UK.
The emerging crop Camelina sativa (L.) Crantz (camelina) is a Brassicaceae oilseed with a rapidly growing reputation for the deployment of advanced lipid biotechnology and metabolic engineering. Camelina is recognised by agronomists for its traits including yield, oil/protein content, drought tolerance, limited input requirements, plasticity and resilience.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Background: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.
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