Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study
| Autor: | Itsuki Osawa, Tadahiro Goto, Takahiro Tabuchi, Hayami K Koga, Yusuke Tsugawa |
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| Rok vydání: | 2022 |
| Předmět: |
Other Medical and Health Sciences
SARS-CoV-2 Depression Happiness Clinical Sciences COVID-19 General Medicine Basic Behavioral and Social Science Brain Disorders Machine Learning Mental Health Good Health and Well Being Clinical Research Behavioral and Social Science Public Health and Health Services Humans Female PUBLIC HEALTH Pandemics Retrospective Studies |
| Zdroj: | BMJ open, vol 12, iss 12 |
| ISSN: | 2044-6055 |
| Popis: | ObjectiveTo investigate determining factors of happiness during the COVID-19 pandemic.DesignObservational study.SettingLarge online surveys in Japan before and during the COVID-19 pandemic.ParticipantsA random sample of 25 482 individuals who are representatives of the Japanese population.Main outcome measureSelf-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness.ResultsAmong the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6–8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; pConclusionUsing machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic. |
| Databáze: | OpenAIRE |
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