Cooking oil fumes (COFs) from cooking with hot oil may contribute to the pathogenesis of lung cancer. Since 2021, occupational lung cancer for individual cafeteria workers has been recognized in South Korea. In this study, we aimed to identify the distribution of lung-imaging reporting and data system (Lung-RADS) among cafeteria workers and to determine factors related to Lung-RADS distribution.

We included 203 female participants who underwent low-dose computed tomography (LDCT) screening at a university hospital and examined the following variables: age, smoking status, second-hand smoke, height, weight, and years of service, mask use, cooking time, heat source, and ventilation. We divided all participants into culinary and non-culinary workers. Binomial logistic regression was conducted to determine the risk factors on LDCT of Category ≥ 3, separately for the overall group and the culinary group.

In this study, Lung-RADS-positive occurred in 17 (8.4%) individuals, all of whom were culinary workers. Binary logistic regression analyses were performed and no variables were found to have a significant impact on Lung-RADS results. In the subgroup analysis, the Lung-RADS-positive, and -negative groups differed only in ventilation. Binary logistic regression showed that the adjusted odds ratio (aOR) of the Lung-RADS-positive group for inappropriate ventilation at the workplace was 14.89 (95% confidence interval [CI]: 3.296–67.231) compared to appropriate ventilation as the reference, and the aOR for electric appliances at home was 4.59 (95% CI: 1.061–19.890) using liquid fuel as the reference.

The rate of Lung-RADS-positive was significantly higher among culinary workers who performed actual cooking tasks than among nonculinary workers. In addition, appropriate ventilation at the workplace made the LDCT results differ. More research is needed to identify factors that might influence LDCT findings among culinary workers, including those in other occupations.

Gig workers, also known as platform workers, are independent workers who are not employed by any particular company. The number of gig economy workers has rapidly increased worldwide in the past decade. There is a dearth of occupational health studies among gig economy workers. We aimed to investigate the association between exposure to violence and job stress in gig economy workers and depressive symptoms.

A total of 955 individuals (521 gig workers and 434 general workers) participated in this study and variables were measured through self-report questionnaires. Depressive symptoms were evaluated by the Patient Health Questionnaire-9 when the score was greater than or equal to 10 points. The odds ratio with 95% confidence interval was calculated using multivariable logistic regression adjusted for age, sex, working hours, education level, exposure to violence and job stress.

19% of gig economy workers reported depressive symptoms, while only 11% of general workers reported the depressive symptoms. In association to depressive symptoms among gig economy workers, the mainly result of odds ratios for depressive symptoms were as follows: 1.81 for workers type, 3.53 for humiliating treatment, 2.65 for sexual harassment, 3.55 for less than three meals per day, 3.69 for feeling too tired to do housework after leaving work.

Gig economic workers are exposed to violence and job stress in the workplace more than general workers, and the proportion of workers reporting depressive symptoms is also high. These factors are associated to depressive symptoms. Furthermore, the gig workers associated between depressive symptoms and exposure to violence, job stress.

Dysmenorrhea and menstrual cycle changes occur in women working shifts. Circadian rhythm disruption and sleep disturbances associated with shift work leads to health problems. We identified chronotypes and the occurrence of insomnia among newly employed university hospital nurses and investigated the association of these factors with menstrual problems.

We conducted pre-placement health examinations for shift workers using self-reported questionnaires between 2018 and 2020. A total of 463 nurses were included in the study. Sociodemographic data, shift work experience, and information on insomnia were collected from health examination data. In addition, details regarding chronotype, dysmenorrhea, irregular and abnormal menstrual cycles, amenorrhea, and contraceptive use were obtained from the questionnaire. Multiple logistic regression analysis was performed to study the association between chronotype, insomnia, and menstrual problems after controlling for age, body mass index, contraceptive use, amenorrhea, and prior shift work.

The prevalence rates of dysmenorrhea, irregular menstrual cycles, and longer menstrual cycles were 23.8%, 14.9%, and 4.1%, respectively. The risk of dysmenorrhea increased in the evening-type (odds ratio [OR]: 3.209; 95% confidence interval [CI]: 1.685–6.113) and those with insomnia (OR: 1.871; 95% CI: 1.074–3.261). Additionally, the risk of an irregular menstrual cycle (OR: 2.698; 95% CI: 1.167–6.237) increased in the evening-type, and the risk of a longer menstrual cycle (OR: 4.008; 95% CI: 1.354–11.864) increased in individuals with insomnia.

Our findings suggest that dysmenorrhea is promoted in the evening-type and insomnia individuals. There may be an increased risk of irregular menstrual cycles among evening-type nurses and an increased risk of longer menstrual cycles among those with insomnia. Therefore, factors such as evening-type and insomnia should be considered for the prevention of menstrual problems in women performing shift work.

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.

The prevalence of multiple job-holding (MJH) is expected to show an upward trend among Korean workers, yet its potential impact on workers’ health remains poorly discussed. This study aimed to explore the association between MJH and experience of physical and psychological symptoms in various aspects among active Korean workers and to identify its differences depending on workers’ gender.

We conducted a population-based cross-sectional study among South Korean workers aged 15–59 using data from the 6th Korean Working Conditions Survey. The final study sample comprised 34,175 participants. MJH status and symptoms, such as musculoskeletal pain, headache or eyestrain, fatigue, insomnia, and anxiety, were measured by self-reported data. Logistic regression analyses were performed on the total sample and gender-stratified groups, with adjustments for the sociodemographic and occupational characteristics of the participants.

One hundred thirty-five men and 103 women were defined as multiple job-holders (MJHers) among the study sample. When adjusted for sociodemographic and occupational characteristics, MJHers suffered more events of headache or eyestrain (odds ratio [OR]: 1.85, 95% confidence interval [CI]: 1.23–2.78) and anxiety (OR: 1.99, 95% CI: 1.05–3.79). Gender-stratified analyses with adjustment showed that among women workers, MJH was associated with musculoskeletal pain (OR: 1.85, 95% CI: 1.02–3.34), headache or eyestrain (OR: 2.53, 95% CI: 1.37–4.67), fatigue (OR: 2.38, 95% CI: 1.29–4.40), insomnia (OR: 1.92, 95% CI: 1.04–3.57), and anxiety (OR: 2.83, 95% CI: 1.11–7.20).

We found a significant association between MJH and various symptoms. Women MJHers were revealed to be a more vulnerable population than their men counterparts. Further research should account for the social context of MJH, and appropriate monitoring and intervention for maintaining the well-being of MJHers are needed.

At present, no short standard questionnaire exists for assessing and comparing major work organization hazards in the workplaces of the United States.

We conducted a series of psychometric tests (content validity, factor analysis, differential-item functioning analysis, reliability, and concurrent validity) to validate and identify core items and scales for major work organization hazards using the data from the 2002–2014 General Social Surveys (GSSs), including the Quality of Worklife (QWL) questionnaire. In addition, an extensive literature review was undertaken to find other major work organization hazards which were not addressed in the GSS.

Although the overall validity of the GSS-QWL questionnaire was satisfactory in the psychometric tests, some GSS-QWL items of work-family conflict, psychological job demands, job insecurity, use of skills on the job, and safety climate scales appeared to be weak. In the end, 33 questions (31 GSS-QWL and 2 GSS) were chosen as the least, but best validated core questions and included in a new short standard questionnaire (called the Healthy Work Survey [HWS]). And their national norms were established for comparisons. Furthermore, based on the literature review, fifteen more questions for assessing other significant work organization hazards (e.g., lack of scheduling control, emotional demands, electronic surveillance, wage theft) were included in the new questionnaire. Thus, the HWS includes 48 questions in total for assessing traditional and emerging work organization hazards, which covers seven theoretical domains: work schedule/arrangement, control, support, reward, demands, safety, and justice.

The HWS is a short standard questionnaire for assessing work organization hazards which can be used as a first step toward the risk management of major work organization hazards in the workplaces of the US.

The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance.

We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state.

Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point.

The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

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.