Workers transitioning from regular to irregular employment are twice as likely to contemplate suicide compared to their counterparts who remain in regular employment. (Image courtesy of Yonhap)
SEOUL, Mar. 13 (Korea Bizwire) – A groundbreaking study has found that workers transitioning from regular to irregular employment are twice as likely to contemplate suicide compared to their counterparts who remain in regular employment.
This significant discovery was made possible through the use of machine learning technology, traditionally employed in the business sector for analyzing profits and other metrics, now providing valuable insights into the health issues faced by vulnerable social groups.
The research, conducted by Yoon Jaehong of Seoul National University Hospital and Kim Ji-hwan of the Seoul National University School of Public Health (co-first authors), along with Kim Seung-sup (corresponding author), was recently published in the Scandinavian Journal of Work, Environment & Health.
The paper sheds light on the causal relationship between employment status changes and mental health.
The team analyzed data from the Korean Welfare Panel Study for 2013 to 2020, focusing on 3,621 wage workers aged 19 and above.
At the onset, all participants were in regular employment, but 10.8% transitioned to irregular employment in the following year.
This change was more prevalent among women, married individuals, those with lower educational levels, service sector employees, small and medium enterprise workers, non-unionized employees, and those with chronic illnesses.
Regular employment was defined by the researchers as a labor condition that meets all four of the following criteria: a contract period exceeding one year, full-time status, direct employment (excluding subcontracted, dispatched workers, and self-employed individuals), and permanent contracts.
Failure to meet any of these criteria classified a worker as irregularly employed.
Participants’ suicidal thoughts and depressive symptoms were measured through self-reported surveys asking if they had seriously considered suicide in the past year, among other questions.
The analysis revealed that individuals who shifted from regular to irregular employment were 2.07 times more likely to consider suicide than those who remained in regular employment, with a statistically significant increase in depressive symptoms as well.
The research team chose machine learning over traditional statistical methods like logistic regression analysis to explore the causal link between employment changes and suicidal ideation.
This innovative approach marks the first time machine learning algorithms have been used to analyze the impact of employment status changes on mental health.
In an interview with Yonhap News, Kim Seung-sup highlighted the motivation behind the study, stating, “Seeing how machine learning, developed alongside computer science, has been predominantly used to maximize Big Tech’s profits, we saw no reason why it couldn’t be employed to research the health of socially vulnerable groups.”
M. H. Lee (mhlee@koreabizwire.com)
