Self-control as an important factor affecting the online learning readiness of Vietnamese medical and health students during the COVID-19 pandemic: a network analysis

Nguyen, Minh Tú; Tran, Binh Thang; Nguyen, Thanh Gia; Phan, Minh Tri; Luong, Thi Thu Tham; Le, Dinh Duong

doi:10.3352/jeehp.2022.19.22

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…readiness for E-learning (Nguyen et al, 2022). The dataset was originally published as a network including six domains measured with 33 items, comprising computer skills, internet skills, online communication, self-learning, self-control, and online motivation.…”

Section: Example Application Of the Averaged And Corrected Nodewise E...mentioning

confidence: 99%

Nodewise parameter aggregation for psychometric networks

Huth,

DeLong,

Waldorp

et al. 2024

Preprint

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Psychometric network estimation often relies on nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs generalized linear models with each node as the outcome. Two regression coefficients are obtained for each link, which are averaged to obtain the edge weight (i.e., the partial association). The nodewise approach has been shown to reveal the true graph structure for multivariate normally distributed variables. However, the regression coefficients are scaled differently than the partial correlations, and therefore their strength may differ. We show that when the correlations of the two predictors with the control variables are different, the averaged regression coefficients are an asymptotically biased estimator of the partial correlation. This is likely to occur when a variable has a high correlation with other nodes in the network (e.g., variables in the same domain) and a lower correlation with another node (e.g., variables in a different domain). We propose a corrected aggregation of the regression weights by taking the square root of the multiplied regression weights. The corrected parameter can recover the true network structure and edge weights for both standardized and unstandardized variables.

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Section: Example Application Of the Averaged And Corrected Nodewise E...mentioning

confidence: 99%

Nodewise parameter aggregation for psychometric networks

Huth,

DeLong,

Waldorp

et al. 2024

Preprint

View full text Add to dashboard Cite

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Distance Education through the Perspective of Students of Anesthetic Technician, Emergency Medical Technician, and Medical Imaging Technician Programs

Kalender,

Kitapçı,

Aksoy

et al. 2024

International Journal of Health Management and Tourism

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Aim: The study aimed to evaluate the distance education process through the perspective of associate degree programs and to identify crucial issues for potential future pandemics. Methods: The study group (n=166) consisted of associate degree students of the Anesthetic Technician, Emergency Medical Technician, and Medical Imaging Technician Programs. Data were collected through an electronic questionnaire regarding main headings: 'Theoretical Courses, Practical Courses, and Professional Development', 'Course Participation and Motivation', 'Measurement and Evaluation', and 'Student Suggestions for Distance Education'. Questions and statements were scored by 5-point Likert. Results: When the effects of distance education ‘on the level of internalization of the profession’ and ‘on the effect on professional competencies’ were assessed, the scores of Medical Imaging Technician Program students were lower than the scores of Anesthetic Technician Program students and Emergency Medical Technician Program students (p

show abstract

Self-control as an important factor affecting the online learning readiness of Vietnamese medical and health students during the COVID-19 pandemic: a network analysis

Cited by 2 publications

References 14 publications

Nodewise parameter aggregation for psychometric networks

Nodewise parameter aggregation for psychometric networks

Distance Education through the Perspective of Students of Anesthetic Technician, Emergency Medical Technician, and Medical Imaging Technician Programs

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