Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.
Objective: This study assessed predictors of stress, anxiety and depression during the COVID‐19 pandemic using a large number of demographic, COVID‐19 context and psychological variables. Methods: Data from 741 adults were drawn from the Boston College daily sleep and well‐being survey. Baseline dem...
| Published in: | British Journal of Clinical Psychology Vol. 63; no. 4; pp. 522 - 543 |
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| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Nov2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180279445&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180279445 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01446657 99B jtl: British Journal of Clinical Psychology issn: 01446657 maglogo: Y pubinfo: dt: Nov2024 vid: 63 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 180279445 178042683 180279445 180279445 10.1111/bjc.12487 180279445 ppf: 522 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study. aug: au: Ganai, Umer Jon Sachdev, Shivani Bhushan, Braj affil: Department of Humanities and Social Sciences, Indian Institute of Technology Kanpur, Kanpur Uttar Pradesh, , India sug: subj: Stress, Psychological Risk Factors Anxiety Risk Factors Depression Risk Factors Machine Learning Prediction Models COVID-19 Pandemic Risk Assessment Human Male Female Adolescence Adult Middle Age Aged Aged, 80 and Over Sleep Psychological Well-Being Random Forest India Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Objective: This study assessed predictors of stress, anxiety and depression during the COVID‐19 pandemic using a large number of demographic, COVID‐19 context and psychological variables. Methods: Data from 741 adults were drawn from the Boston College daily sleep and well‐being survey. Baseline demographics, the long version of the daily surveys and the round one assessment of the survey were utilized for the present study. A Gaussian graphical model (GGM) was estimated as a feature selection technique on a subset of ordinal/continuous variables. An ensemble Random Forest (RF) machine learning algorithm was used for prediction. Results: GGM was found to be an efficient feature selection method and supported the findings derived from the RF machine learning model. Psychological variables were significant predictors of stress, anxiety and depression, while demographic and COVID‐19‐related factors had minimal predictive value. The outcome variables were mutually predictive of each other, and negative affect and subjective sleep quality were the common predictors of these outcomes of stress, anxiety, and depression. Conclusion: The study identifies risk factors for adverse mental health outcomes during the pandemic and informs interventions to mitigate the impact on mental health. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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