A Comparative Study to Assess the Level of Internet Addiction Among B.Sc. Nursing and GNM Students at Rama College of Nursing Kanpur with a view to Develop Information Booklet

Thirusangu, Senthil; Kumar, Abhishek; Akansha,; Yadav, Akansha; Kumari, Anju Prabha; Mishra, Anuradha; Shakya, Subhi; Patel, Tushar A

doi:10.5958/2349-2996.2020.00075.0

Cited by 4 publications

(2 citation statements)

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“…A Turkish study of 1558 high school students reported a PIU prevalence estimate of 21.1% [24] . However, one Indian study [25] reported a prevalence estimate of 47.0% among 470 nursing students. All of the aforementioned recent studies used the same instrument (IAT) and cut score (i.e., ≥50) but still produced inconsistent results which might be attributed – at least partially – to methodological and cultural differences as well as the non-representativeness of the different types of cohorts sampled.…”

Section: The Inconsistency Of Piu Prevalence Estimatesmentioning

confidence: 99%

Prevalence of problematic Internet use during the coronavirus disease 2019 pandemic

Burkauskas¹,

Gečaitė-Stončienė²,

Demetrovics³

et al. 2022

Current Opinion in Behavioral Sciences

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Section: The Inconsistency Of Piu Prevalence Estimatesmentioning

confidence: 99%

Prevalence of problematic Internet use during the coronavirus disease 2019 pandemic

Burkauskas¹,

Gečaitė-Stončienė²,

Demetrovics³

et al. 2022

Current Opinion in Behavioral Sciences

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“…In order to get network parameters, we use an unsupervised greedy algorithm to pre-train the network model. First, the first layer RBM1 is trained, a vector is generated in the visual layer of RBM1, and the generated value is transmitted to the hidden layer; Then, Thirusangu et al reconstructed the input signal of the visual layer 14 , and the data of the visual layer is randomly extracted and transmitted to the hidden layer. It trains each layer by using the maximum function learning method and combining deep learning network and training mechanism.…”

Section: Analysis Of the Research Status Of Machine Learningmentioning

confidence: 99%

A new model of Internet addiction tendency based on machine learning

Zhi-jun

zhang

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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This paper proposes a new model to analysis multi factors that affect internet addiction. Firstly, based on the latest research status and achievements of machine learning, this paper constructs a multi-factor weighted analysis model of Internet addiction tendency. Secondly, in order to analyze the determinants of internet addiction more effectively, this paper designs a deep belief network structure based on the method of machine. Through the training of this network, we can get more accurate weight distribution of various factors in the network structure, so as to extract more effective convolution features of multi-factors. Finally, it gives the compared experimental results which show that this new model can make the internet addiction tendency analysis method be more accurate and effective.

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Nursing and Midwifery Students’ Attitudes Towards Research: A Descriptive Study

Amoo

Gbadamosi²

2021

AJNER

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Background: Some studies found that nurses are not actively involved in research. To address this issue, it is important to investigate the structure of training that nurses got while at school of nursing. To promote evidence-based practice, nursing and midwifery students must be actively involved in research. Several studies have reported on the attitude of nursing students towards research in different countries. However, no study has assessed attitude of both nursing and midwifery students towards research in Nigeria, using a valid and reliable measure- attitude towards research scale. Purpose: The aim of this study was to assess diploma nursing and midwifery students’ attitude towards research, and their participation in research-related activities. Methods: Descriptive cross-sectional design was used to conduct the study, where 91 nursing and midwifery students who were purposively selected, provided data for the study. Students in graduating classes were included in the study. Papanastasiou’s (2005) Attitude Towards Research scale questionnaire was adopted and used to collect data from participants. Ethical approvals were received from research ethics committee at School of Nursing Ilaro and research ethics committees at other participating institutions. Results: Majority of participants were females, and nursing students. Most participants agreed that research was useful in their career. However, they perceived research to be stressful, complicated, difficult and a complex subject. About 63% of participants had positive attitude towards research while 37% have negative attitude. Nonetheless, only 39% participated in research-related activities. Conclusions: Students’ participation in research-related activities was low. Nursing and midwifery faculties should adopt measures including research workshops, seminars, and conferences to improve student participation in research.

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A Comparative Study to Assess the Level of Internet Addiction Among B.Sc. Nursing and GNM Students at Rama College of Nursing Kanpur with a view to Develop Information Booklet

Cited by 4 publications

References 0 publications

Prevalence of problematic Internet use during the coronavirus disease 2019 pandemic

Prevalence of problematic Internet use during the coronavirus disease 2019 pandemic

A new model of Internet addiction tendency based on machine learning

Nursing and Midwifery Students’ Attitudes Towards Research: A Descriptive Study

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