Prediction of adolescent subjective well-being: A machine learning approach

Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach

Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.

Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study

OBJECTIVE

To investigate determining factors of happiness during the COVID-19 pandemic.

DESIGN

Observational study.

SETTING

Large online surveys in Japan before and during the COVID-19 pandemic.

PARTICIPANTS

A random sample of 25 482 individuals who are representatives of the Japanese population.

MAIN OUTCOME MEASURE

Self-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.

RESULTS

Among 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; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.

CONCLUSION

Using 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.

A Scientometric Systematic Review of Entrepreneurial Wellbeing Knowledge Production

This article presents a scientometric study regarding entrepreneurship and its relationship with wellbeing. The study presents a systematic review and measures impact and relational character to identify the relevance of countries, research organizations, and authors in the field of entrepreneurial wellbeing. The study poses the following research questions: What is the nature of the evolution of scientific knowledge in the entrepreneurial wellbeing field? What is the nature of the concentration in terms of geographical distribution and co-authorship level of knowledge production in the entrepreneurial wellbeing field? What are the knowledge trends in knowledge production for entrepreneurial wellbeing literature? The contribution of this research is two-fold. First, in terms of methodology, it contributes study into the use of a more robust approach to search for the scientometric trends about entrepreneurship wellbeing in addition to the PRISMA review tools and the PICOS eligibility criteria. Secondly, the study presents research updates in the search for results for the last 2 years of knowledge production. This upgrade is particularly important in a research field that presents exponential growth, where 2019 and 2020 presented almost double the amount of knowledge production compared to 2017 and 2018.