1.Data profile: Korean Work, Sleep, and Health Study (KWSHS)
Seong-Sik CHO ; Jeehee MIN ; Heejoo KO ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2025;37(1):e3-
The Korean Work, Sleep, and Health Study (KWSHS) was launched in 2022 as a longitudinal panel study to examine the interactions between work conditions, sleep health, and labour market performance among the Korean workforce. Baseline data were collected from 5,517 participants aged 19 to 70, encompassing diverse occupations. Follow-up surveys occur biannually, accommodating seasonal variations in sleep and health dynamics. To ensure stability, refreshment samples were integrated in later waves, maintaining a cohort size of 5,783 participants in wave 5. Key data include socio-demographics, employment characteristics, sleep patterns, health outcomes, and workplace performance. Early findings highlight critical associations, such as the adverse effects of occupational physical activity on productivity, the impact of emotional labour on health-related productivity loss, and the significance of sleep disruptions on mental health. The cohort’s design enables detailed analyses of longitudinal and cross-sectional trends, offering insights into how changing work environments influence health and productivity. The KWSHS could serve as a vital resource for evidence-based interventions aimed at improving occupational health and productivity in Korea's evolving labour landscape. Data access is available through the study’s principal investigator upon request.
2.Data profile: Korean Work, Sleep, and Health Study (KWSHS)
Seong-Sik CHO ; Jeehee MIN ; Heejoo KO ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2025;37(1):e3-
The Korean Work, Sleep, and Health Study (KWSHS) was launched in 2022 as a longitudinal panel study to examine the interactions between work conditions, sleep health, and labour market performance among the Korean workforce. Baseline data were collected from 5,517 participants aged 19 to 70, encompassing diverse occupations. Follow-up surveys occur biannually, accommodating seasonal variations in sleep and health dynamics. To ensure stability, refreshment samples were integrated in later waves, maintaining a cohort size of 5,783 participants in wave 5. Key data include socio-demographics, employment characteristics, sleep patterns, health outcomes, and workplace performance. Early findings highlight critical associations, such as the adverse effects of occupational physical activity on productivity, the impact of emotional labour on health-related productivity loss, and the significance of sleep disruptions on mental health. The cohort’s design enables detailed analyses of longitudinal and cross-sectional trends, offering insights into how changing work environments influence health and productivity. The KWSHS could serve as a vital resource for evidence-based interventions aimed at improving occupational health and productivity in Korea's evolving labour landscape. Data access is available through the study’s principal investigator upon request.
3.Data profile: Korean Work, Sleep, and Health Study (KWSHS)
Seong-Sik CHO ; Jeehee MIN ; Heejoo KO ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2025;37(1):e3-
The Korean Work, Sleep, and Health Study (KWSHS) was launched in 2022 as a longitudinal panel study to examine the interactions between work conditions, sleep health, and labour market performance among the Korean workforce. Baseline data were collected from 5,517 participants aged 19 to 70, encompassing diverse occupations. Follow-up surveys occur biannually, accommodating seasonal variations in sleep and health dynamics. To ensure stability, refreshment samples were integrated in later waves, maintaining a cohort size of 5,783 participants in wave 5. Key data include socio-demographics, employment characteristics, sleep patterns, health outcomes, and workplace performance. Early findings highlight critical associations, such as the adverse effects of occupational physical activity on productivity, the impact of emotional labour on health-related productivity loss, and the significance of sleep disruptions on mental health. The cohort’s design enables detailed analyses of longitudinal and cross-sectional trends, offering insights into how changing work environments influence health and productivity. The KWSHS could serve as a vital resource for evidence-based interventions aimed at improving occupational health and productivity in Korea's evolving labour landscape. Data access is available through the study’s principal investigator upon request.
4.Association between the safety climate and occupational injury in the Korean working population: a cross-sectional study
Jeehee MIN ; Tae-Won JANG ; Hye-Eun LEE ; Mo-Yeol KANG ; Seong-Sik CHO
Epidemiology and Health 2024;46(1):e2024082-
OBJECTIVES:
Preventing occupational injuries remains a significant challenge in Korea. A positive safety climate can contribute to reducing workplace injuries. However, the impact of safety climate on preventing occupational injuries among the Korean workforce has not been adequately explored. Therefore, this study aimed to investigate the relationship between the perceived safety climate and occupational injuries within the Korean working population.
METHODS:
This study used baseline data from the Korean Work, Sleep, and Health Study (KWSH). The safety climate was measured using the brief version of the Nordic Safety Climate Questionnaire. Occupational injury was determined by whether injuries or accidents had occurred at workplaces in the past year. Logistic regression analysis was performed to examine the association between the safety climate and occupational injury.
RESULTS:
Participants who reported an unfavorable workplace safety climate were more likely to experience occupational injuries. Multiple logistic regression analysis revealed that the adjusted odds ratio (OR) for occupational injuries in an unfavorable safety climate was 2.20 (95% confidence interval [CI], 1.38 to 3.51) compared to a favorable safety climate. Specifically, factors such as “not encouraging employees to follow safety rules when on a tight schedule” (OR, 2.02; 95% CI, 1.25 to 3.24) and “not helping each other work safely” (OR, 1.98; 95% CI, 1.17 to 3.25) were significantly associated with occupational injuries.
CONCLUSIONS
An unfavorable safety climate was associated with increased occupational injuries among Korean workers. Improving the safety climate in the workplace may reduce occupational injuries in Korea.
5.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
6.Exploring the impact of age and socioeconomic factors on health-related unemployment using propensity score matching: results from Korea National Health and Nutrition Examination Survey (2015–2017)
Ye-Seo LEE ; Dong-Wook LEE ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2024;36(1):e16-
Previous reports showed that age and socioeconomic factors mediated health-related unemployment. However, those studies had limitations controlling for confounding factors. This study examines age and socioeconomic factors contributing to health-related unemployment using propensity score matching (PSM) to control for various confounding variables. Data were obtained from the Korean National Health and Nutrition Examination Survey (KNHANES) from 2015–2017. We applied a 1:1 PSM to align health factors, and examined the association between health-related unemployment and age or socioeconomic factors through conditional logistic regression. The health-related unemployment group was compared with the employment group. Among the 9,917 participants (5,817 women, 4,100 men), 1,182 (853 women, 329 men) were in the health-related unemployment group. Total 911 pairs (629 women pairs and 282 men pairs) were retained after PSM for health factors. The results of conditional logistic regression showed that older age, low individual and household income levels, low education level, receipt of the Basic Livelihood Security Program benefits and longest-held job characteristics were linked to health-related unemployment, despite having similar health levels. Older age and low socioeconomic status can increase the risk of health-related unemployment, highlighting the presence of age discrimination and socioeconomic inequality. These findings underscore the importance of proactive management strategies aimed at addressing these disparities, which are crucial for reducing the heightened risk of health-related unemployment.
7.Association between work from home and health-related productivity loss among Korean employees
Hyo Jeong KIM ; Dong Wook LEE ; Jaesung CHOI ; Yun-Chul HONG ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2024;36(1):e13-
After the coronavirus disease 2019 pandemic, the widespread adoption of working from home, or teleworking, has prompted extensive research regarding its effects on work productivity and the physical and mental health of employees. In this context, our study aimed to investigate the association between working from home and health-related productivity loss (HRPL). An online survey was conducted with a sample of 1,078 workers. HRPL was estimated by the Work Productivity and Activity Impairment Questionnaire: General Health version. Workers that have been working from home in the last 6 months were categorized into the “work from home” group. Generalized linear models were used to compare the mean difference of HRPL between “work from home” and “commuters” group. Stratified analyses were conducted based on various factors including gender, age, income level, occupation, education level, previous diagnosis of chronic disease, presence of preschool children, living in studio apartment, living alone, commuting time, working hours and regular exercise. The overall HRPL was higher in the “work from home” group than in the “commuters” group with a mean difference of 4.05 (95% confidence interval [CI]: 0.09–8.01). In the stratified analyses, significant differences were observed in workers with chronic diseases (mean difference: 8.23, 95% CI: 0.38–16.09), who do not live alone (mean difference: 4.84, 95% CI: 0.35–9.33), and workers that do not exercise regularly (mean difference: 4.96, 95% CI: 0.12–9.80). Working from home is associated with an increased HRPL in the Korean working population, especially among those with chronic diseases, those who do not live alone, and those who do not exercise regularly.
8.Association between the safety climate and occupational injury in the Korean working population: a cross-sectional study
Jeehee MIN ; Tae-Won JANG ; Hye-Eun LEE ; Mo-Yeol KANG ; Seong-Sik CHO
Epidemiology and Health 2024;46(1):e2024082-
OBJECTIVES:
Preventing occupational injuries remains a significant challenge in Korea. A positive safety climate can contribute to reducing workplace injuries. However, the impact of safety climate on preventing occupational injuries among the Korean workforce has not been adequately explored. Therefore, this study aimed to investigate the relationship between the perceived safety climate and occupational injuries within the Korean working population.
METHODS:
This study used baseline data from the Korean Work, Sleep, and Health Study (KWSH). The safety climate was measured using the brief version of the Nordic Safety Climate Questionnaire. Occupational injury was determined by whether injuries or accidents had occurred at workplaces in the past year. Logistic regression analysis was performed to examine the association between the safety climate and occupational injury.
RESULTS:
Participants who reported an unfavorable workplace safety climate were more likely to experience occupational injuries. Multiple logistic regression analysis revealed that the adjusted odds ratio (OR) for occupational injuries in an unfavorable safety climate was 2.20 (95% confidence interval [CI], 1.38 to 3.51) compared to a favorable safety climate. Specifically, factors such as “not encouraging employees to follow safety rules when on a tight schedule” (OR, 2.02; 95% CI, 1.25 to 3.24) and “not helping each other work safely” (OR, 1.98; 95% CI, 1.17 to 3.25) were significantly associated with occupational injuries.
CONCLUSIONS
An unfavorable safety climate was associated with increased occupational injuries among Korean workers. Improving the safety climate in the workplace may reduce occupational injuries in Korea.
9.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
10.Exploring the impact of age and socioeconomic factors on health-related unemployment using propensity score matching: results from Korea National Health and Nutrition Examination Survey (2015–2017)
Ye-Seo LEE ; Dong-Wook LEE ; Mo-Yeol KANG
Annals of Occupational and Environmental Medicine 2024;36(1):e16-
Previous reports showed that age and socioeconomic factors mediated health-related unemployment. However, those studies had limitations controlling for confounding factors. This study examines age and socioeconomic factors contributing to health-related unemployment using propensity score matching (PSM) to control for various confounding variables. Data were obtained from the Korean National Health and Nutrition Examination Survey (KNHANES) from 2015–2017. We applied a 1:1 PSM to align health factors, and examined the association between health-related unemployment and age or socioeconomic factors through conditional logistic regression. The health-related unemployment group was compared with the employment group. Among the 9,917 participants (5,817 women, 4,100 men), 1,182 (853 women, 329 men) were in the health-related unemployment group. Total 911 pairs (629 women pairs and 282 men pairs) were retained after PSM for health factors. The results of conditional logistic regression showed that older age, low individual and household income levels, low education level, receipt of the Basic Livelihood Security Program benefits and longest-held job characteristics were linked to health-related unemployment, despite having similar health levels. Older age and low socioeconomic status can increase the risk of health-related unemployment, highlighting the presence of age discrimination and socioeconomic inequality. These findings underscore the importance of proactive management strategies aimed at addressing these disparities, which are crucial for reducing the heightened risk of health-related unemployment.

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