wow-inequalities/02-data/intermediate/wos_sample/bb780f171992bbd6a1892cae02e190f7-bakkeli-nan-zou/info.yaml

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2023-09-28 14:46:10 +00:00
abstract: 'Background and purpose: The COVID-19 pandemic has posed considerable
challenges to people''s mental health, and the prevalence of anxiety and
depression increased substantially during the pandemic. Early detection
of potential depression is crucial for timely preventive interventions;
therefore, there is a need for depression prediction. Data and methods:
This study was based on survey data collected from 5001 Norwegians (3001
in 2020 and 2000 in 2021). Machine learning models were used to predict
depression risk and to select models with the best performance for each
pandemic phase. Probability thresholds were chosen based on
cost-sensitive analysis, and measures such as accuracy (ACC) and the
area under the receiver operating curve (AUC) were used to evaluate the
models'' performance. Results: The study found that decision tree models
and regularised regressions had the best performance in both 2020 and
2021. For the 2020 predictions, the highest accuracies were obtained
using gradient boosting machines (ACC = 0.72, AUC = 0.74) and random
forest algorithm (ACC = 0.71, AUC = 0.75). For the 2021 predictions, the
random forest (ACC = 0.76, AUC = 0.78) and elastic net regularisation
(ACC = 0.76, AUC = 0.78) exhibited the best performances. Highly ranked
predictors of depression that remained stable over time were
self-perceived exposure risks, income, compliance with nonpharmaceutical
interventions, frequency of being outdoors, contact with family and
friends and work-life conflict. While epidemiological factors (having
COVID symptoms or having close contact with the infected) influenced the
level of psychological distress to a larger extent in the relatively
early stage of pandemic, the importance of socioeconomic factors
(gender, age, household type and employment status) increased
substantially in the later stage. Conclusion: Machine learning models
consisting of demographic, socioeconomic, behavioural and
epidemiological features can be used for fast `first-hand'' screening to
diagnose mental health problems. The models may be helpful for
stakeholders and healthcare providers to provide early diagnosis and
intervention, as well as to provide insight into forecasting which
social groups are more vulnerable to mental illness in which social
settings.'
affiliation: 'Bakkeli, NZ (Corresponding Author), Oslo Metropolitan Univ, Ctr Res
Pandem \& Soc, Consumpt Studies Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.
Bakkeli, Nan Zou, Oslo Metropolitan Univ, Ctr Res Pandem \& Soc, Consumpt Studies
Norway, POB 4,St Olavs Plass, N-0130 Oslo, Norway.'
article-number: 08944393211069622
author: Bakkeli, Nan Zou
author-email: Nan.Bakkeli@OsloMet.no
author_list:
- family: Bakkeli
given: Nan Zou
da: '2023-09-28'
doi: 10.1177/08944393211069622
earlyaccessdate: FEB 2022
eissn: 1552-8286
files: []
issn: 0894-4393
journal: SOCIAL SCIENCE COMPUTER REVIEW
keywords: 'mental health; depression; COVID-19; social determinants of health;
inequality; machine learning'
keywords-plus: 'MENTAL-HEALTH; SOCIAL DETERMINANTS; PRIMARY-CARE; DEPRESSION; ANXIETY;
POPULATION; DISORDERS; WORKERS; IMPACT; WUHAN'
language: English
month: AUG
number: '4'
number-of-cited-references: '70'
orcid-numbers: Bakkeli, Nan/0000-0002-4089-020X
pages: 1227-1251
papis_id: 6a9f4be6ca6f5635ae9697191a17ed7f
ref: Bakkeli2023predictingpsychologi
times-cited: '1'
title: 'Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic
Factors Matter?'
2023-10-01 08:15:07 +00:00
type: article
2023-09-28 14:46:10 +00:00
unique-id: WOS:000769618400001
usage-count-last-180-days: '1'
usage-count-since-2013: '6'
volume: '41'
web-of-science-categories: 'Computer Science, Interdisciplinary Applications; Information
Science \&
Library Science; Social Sciences, Interdisciplinary'
year: '2023'