Aim:This study aims to evaluate demands for general and pharmacological knowledge and training related to intravenous(IV)therapy among pediatric nurses.Materials and Methods:This multicentric cross‑sectional study inv...Aim:This study aims to evaluate demands for general and pharmacological knowledge and training related to intravenous(IV)therapy among pediatric nurses.Materials and Methods:This multicentric cross‑sectional study involved 12,707 pediatric nurses from 100 hospitals in China.A questionnaire was used to assess information about IV drug administration training received,and the demands for pharmacology‑related knowledge,and corresponding methods of acquisition.A generalized linear model using Logit link function was employed to assess relationships between factors and multivariate analysis was conducted.Results:More than 99%of participants showed their training demands for IV therapy training.Pediatric nurses’demands and methods for acquiring IV therapy knowledge and the knowledge related to IV therapy pharmacology have significant differences in social demographic factors,previous knowledge,whether they have received training or not,and other factors(all P<0.05).Received a needlestick injury in the past month(P=0.007)and knowledge acquired through in‑hospital or out‑of‑hospital training(P=0.039)were factors that reduced the demands for methods to acquire further pharmacology‑related knowledge of IV therapy.Working in internal medicine(P=0.025)and not having experienced a needlestick injury in the past year(P=0.007)reduced the demands for IV therapy knowledge.Attended hospital(P=0.007)or departmental meetings(P=0.009),being pediatric primary nurses(P=0.044),and studied special IV guidelines(P=0.006)reduced the desire for methods to acquire IV therapy knowledge.Conclusion:There was a high demand for greater general and pharmacological knowledge related to IV therapy among pediatric nurses.Resources should be coordinated to provide ongoing training to nurses to improve the quality of IV care.展开更多
Background:Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models.These constraint...Background:Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models.These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts.Transfer learning(TL),a machine learning technique,emerges as a powerful solution by utilizing knowledge from pretrained models to enhance the performance of new models,offering promise across various healthcare domains.Despite its conceptual origins in the 1990s,the application of TL in medical research has remained limited,especially beyond image analysis.This review aims to analyze TL applications,highlight overlooked techniques,and suggest improvements for future healthcare research.Methods:Following the PRISMA-ScR guidelines,we conducted a search for published articles that employed TL with structured clinical or biomedical data by searching the SCOPUS,MEDLINE,Web of Science,Embase,and CINAHL databases.Results:We screened 5,080 papers,with 86 meeting the inclusion criteria.Among these,only 2%(2 of 86)utilized external studies,and 5%(4 of 86)addressed scenarios involving multi-site collaborations with privacy constraints.Conclusions:To achieve actionable TL with structured medical data while addressing regional disparities,inequality,and privacy constraints in healthcare research,we advocate for the careful identification of appropriate source data and models,the selection of suitable TL frameworks,and the validation of TL models with proper baselines.展开更多
文摘Aim:This study aims to evaluate demands for general and pharmacological knowledge and training related to intravenous(IV)therapy among pediatric nurses.Materials and Methods:This multicentric cross‑sectional study involved 12,707 pediatric nurses from 100 hospitals in China.A questionnaire was used to assess information about IV drug administration training received,and the demands for pharmacology‑related knowledge,and corresponding methods of acquisition.A generalized linear model using Logit link function was employed to assess relationships between factors and multivariate analysis was conducted.Results:More than 99%of participants showed their training demands for IV therapy training.Pediatric nurses’demands and methods for acquiring IV therapy knowledge and the knowledge related to IV therapy pharmacology have significant differences in social demographic factors,previous knowledge,whether they have received training or not,and other factors(all P<0.05).Received a needlestick injury in the past month(P=0.007)and knowledge acquired through in‑hospital or out‑of‑hospital training(P=0.039)were factors that reduced the demands for methods to acquire further pharmacology‑related knowledge of IV therapy.Working in internal medicine(P=0.025)and not having experienced a needlestick injury in the past year(P=0.007)reduced the demands for IV therapy knowledge.Attended hospital(P=0.007)or departmental meetings(P=0.009),being pediatric primary nurses(P=0.044),and studied special IV guidelines(P=0.006)reduced the desire for methods to acquire IV therapy knowledge.Conclusion:There was a high demand for greater general and pharmacological knowledge related to IV therapy among pediatric nurses.Resources should be coordinated to provide ongoing training to nurses to improve the quality of IV care.
基金supported by the Duke/Duke-NUS Collaboration grant.
文摘Background:Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models.These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts.Transfer learning(TL),a machine learning technique,emerges as a powerful solution by utilizing knowledge from pretrained models to enhance the performance of new models,offering promise across various healthcare domains.Despite its conceptual origins in the 1990s,the application of TL in medical research has remained limited,especially beyond image analysis.This review aims to analyze TL applications,highlight overlooked techniques,and suggest improvements for future healthcare research.Methods:Following the PRISMA-ScR guidelines,we conducted a search for published articles that employed TL with structured clinical or biomedical data by searching the SCOPUS,MEDLINE,Web of Science,Embase,and CINAHL databases.Results:We screened 5,080 papers,with 86 meeting the inclusion criteria.Among these,only 2%(2 of 86)utilized external studies,and 5%(4 of 86)addressed scenarios involving multi-site collaborations with privacy constraints.Conclusions:To achieve actionable TL with structured medical data while addressing regional disparities,inequality,and privacy constraints in healthcare research,we advocate for the careful identification of appropriate source data and models,the selection of suitable TL frameworks,and the validation of TL models with proper baselines.
