<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff workability prediction model based on R-GCN-GRU was constructed. In the process of community nursing staff workability feature extraction, the attention mechanism is introduced, combined with the degree of association between the captured nodes of the R-GCN network and the long-term memory capacity of the GRU network, and the model optimization is carried out using the cross-entropy loss function. Finally, the workability of community caregivers in a city in Guangdong Province was predicted to verify the accuracy of the model from multiple perspectives. The results showed that clinical handling ability, keen observation ability, and communication ability were more valued by most caregivers, and their selection rates all reached 98.4%. On the other hand, clinical research, organizational management, and innovation abilities were relatively low. In the ability prediction of individual characteristics, the highest income personnel’s working ability was second only to the lowest salary personnel reaching 44.61±6.03. The working ability of older age and higher-position nursing staff, and nursing staff with more than 25 years of service reached 45.62±6.14, 48.30±5.22, and 45.86±5.52, respectively.</p>
</abstract>

To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff workability prediction model based on R-GCN-GRU was constructed. In the process of community nursing staff workability feature extraction, the attention mechanism is introduced, combined with the degree of association between the captured nodes of the R-GCN network and the long-term memory capacity of the GRU network, and the model optimization is carried out using the cross-entropy loss function. Finally, the workability of community caregivers in a city in Guangdong Province was predicted to verify the accuracy of the model from multiple perspectives. The results showed that clinical handling ability, keen observation ability, and communication ability were more valued by most caregivers, and their selection rates all reached 98.4%. On the other hand, clinical research, organizational management, and innovation abilities were relatively low. In the ability prediction of individual characteristics, the highest income personnel’s working ability was second only to the lowest salary personnel reaching 44.61±6.03. The working ability of older age and higher-position nursing staff, and nursing staff with more than 25 years of service reached 45.62±6.14, 48.30±5.22, and 45.86±5.52, respectively.

Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers

Youjiang Medical University for Nationalities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff...

Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 20 déc. 2023

Accepté: 27 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0206

Mots clés
Deep learning, Cross-entropy loss function, R-GCN-GRU network, Competency prediction model, Community caregivers

© 2024 Juan Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 20 déc. 2023

Accepté: 27 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0206

Mots clésDeep learning, Cross-entropy loss function, R-GCN-GRU network, Competency prediction model, Community caregivers

© 2024 Juan Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Deep learning, Cross-entropy loss function, R-GCN-GRU network, Competency prediction model, Community caregivers