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.

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.