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Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)

BACKGROUND: Since expanding the use of appropriate and effective health technologies will greatly benefit the diagnosis and treatment of some major diseases at an early stage, understanding the mechanism of technology use is crucial for its successful implementation. Few previous studies focused on...

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Autores principales: Deng, Qingwen, Wang, Yueqin, Liu, Wenbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785222/
https://www.ncbi.nlm.nih.gov/pubmed/35082541
http://dx.doi.org/10.2147/RMHP.S344923
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author Deng, Qingwen
Wang, Yueqin
Liu, Wenbin
author_facet Deng, Qingwen
Wang, Yueqin
Liu, Wenbin
author_sort Deng, Qingwen
collection PubMed
description BACKGROUND: Since expanding the use of appropriate and effective health technologies will greatly benefit the diagnosis and treatment of some major diseases at an early stage, understanding the mechanism of technology use is crucial for its successful implementation. Few previous studies focused on the healthcare providers and involved multi-facets factors at individual, technical, organizational, and environmental levels. PURPOSE: To examine the influencing mechanism of technology use among Chinese physicians by integrating multilevel factors, Des-gamma-Carboxy Prothrombin (DCP) was taken as an example. METHODS: Through multistage random sampling, a cross-sectional questionnaire survey was conducted among physicians in charge of direct use of DCP of sampled secondary and tertiary hospitals. Since the sample data comprised two hierarchical levels (physicians and hospitals), multilevel structural equation modeling was used to link five aspects of factors with physicians’ technology use and estimate the effects. RESULTS: Totally, 229 physicians completed the investigation. The use of DCP appears to be at a relatively low level. Intra-class coefficients of the null model (unadjusted baseline model) suggested that physicians’ DCP use has a significant variation between hospitals. The final model identified that value cognition (B = 0.447, P < 0.01), experienced organizational practice (B = 0.203, P < 0.05), and perceived organizational atmosphere (B = −0.237, P < 0.01) contributed directly to physicians’ DCP use. Additionally, technical assessment, perceived organizational atmosphere, and perceived environmental pressure had indirect impacts on physicians’ DCP use that were mediated by value cognition and experienced organizational practice (P < 0.05). CONCLUSION: This study incorporated and determined the significant direct or indirect role of value cognition, technical assessment, experienced organizational practice, perceived organizational atmosphere, and perceived environmental pressure. This influencing mechanism with integrated multilevel factors could serve as a theoretical basis for tailoring interventions to promote technology use among Chinese physicians.
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spelling pubmed-87852222022-01-25 Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP) Deng, Qingwen Wang, Yueqin Liu, Wenbin Risk Manag Healthc Policy Original Research BACKGROUND: Since expanding the use of appropriate and effective health technologies will greatly benefit the diagnosis and treatment of some major diseases at an early stage, understanding the mechanism of technology use is crucial for its successful implementation. Few previous studies focused on the healthcare providers and involved multi-facets factors at individual, technical, organizational, and environmental levels. PURPOSE: To examine the influencing mechanism of technology use among Chinese physicians by integrating multilevel factors, Des-gamma-Carboxy Prothrombin (DCP) was taken as an example. METHODS: Through multistage random sampling, a cross-sectional questionnaire survey was conducted among physicians in charge of direct use of DCP of sampled secondary and tertiary hospitals. Since the sample data comprised two hierarchical levels (physicians and hospitals), multilevel structural equation modeling was used to link five aspects of factors with physicians’ technology use and estimate the effects. RESULTS: Totally, 229 physicians completed the investigation. The use of DCP appears to be at a relatively low level. Intra-class coefficients of the null model (unadjusted baseline model) suggested that physicians’ DCP use has a significant variation between hospitals. The final model identified that value cognition (B = 0.447, P < 0.01), experienced organizational practice (B = 0.203, P < 0.05), and perceived organizational atmosphere (B = −0.237, P < 0.01) contributed directly to physicians’ DCP use. Additionally, technical assessment, perceived organizational atmosphere, and perceived environmental pressure had indirect impacts on physicians’ DCP use that were mediated by value cognition and experienced organizational practice (P < 0.05). CONCLUSION: This study incorporated and determined the significant direct or indirect role of value cognition, technical assessment, experienced organizational practice, perceived organizational atmosphere, and perceived environmental pressure. This influencing mechanism with integrated multilevel factors could serve as a theoretical basis for tailoring interventions to promote technology use among Chinese physicians. Dove 2022-01-18 /pmc/articles/PMC8785222/ /pubmed/35082541 http://dx.doi.org/10.2147/RMHP.S344923 Text en © 2022 Deng et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Deng, Qingwen
Wang, Yueqin
Liu, Wenbin
Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title_full Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title_fullStr Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title_full_unstemmed Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title_short Using Multilevel Structural Equation Modeling (MSEM) to Identify the Predictors and Influencing Mechanism of Technology Use Among Chinese Physicians: An Example from Des-Gamma-Carboxy Prothrombin (DCP)
title_sort using multilevel structural equation modeling (msem) to identify the predictors and influencing mechanism of technology use among chinese physicians: an example from des-gamma-carboxy prothrombin (dcp)
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785222/
https://www.ncbi.nlm.nih.gov/pubmed/35082541
http://dx.doi.org/10.2147/RMHP.S344923
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