Add wos sample results library

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

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+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
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+  self-perceived exposure risks, income, compliance with nonpharmaceutical
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+  interventions, frequency of being outdoors, contact with family and
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+  friends and work-life conflict. While epidemiological factors (having
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+  COVID symptoms or having close contact with the infected) influenced the
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+  level of psychological distress to a larger extent in the relatively
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+  early stage of pandemic, the importance of socioeconomic factors
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+  (gender, age, household type and employment status) increased
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+  substantially in the later stage. Conclusion: Machine learning models
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+  consisting of demographic, socioeconomic, behavioural and
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+  epidemiological features can be used for fast `first-hand'' screening to
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+  diagnose mental health problems. The models may be helpful for
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+  stakeholders and healthcare providers to provide early diagnosis and
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+  intervention, as well as to provide insight into forecasting which
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+  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?'
+type: Article
+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'