Gender Differences in the Quality of Life of Formal Workers.

BACKGROUND: This study aimed to assess the quality of life associated with gender inequalities in formal workers and to determine the effect of sociodemographic, clinical, and behavioral factors on the quality of life (QOL). METHODS: This cross-sectional study involved 1270 workers. Quality of life was measured using the EUROHIS-QOL 8-Item and assessed in terms of psychological, environmental, social, and physical domains, while demographic, socioeconomic, behavioral, and clinical variables served as explanatory variables. Analyses were performed using an ordinal logistic regression model whose significance level was 5%. RESULTS: Of the participants, 80.2% were men, and 19.8% were women; the mean age was 34 (standard deviation: ±10) and 32 (±9) years, respectively. In all prediction scenarios, men were more likely to have a higher quality of life, especially in the physical (odds ratio: 2.16; 95% confidence interval: 1.60-2.93) and psychological (odds ratio: 2.09; 95% confidence interval: 1.51-2.91) domains. CONCLUSIONS: Men and women had significantly different levels of quality of life, and sociodemographic, clinical, and behavioral variables partially clarified these differences, which were possibly established by a socio-historical process of construction of the work role determined by gender issues.

Quality of Life and Associated Factors in Young Workers.

BACKGROUND: This study aimed to identify the factors associated with the quality of life of young workers of a Social Work of Industry Unit. METHODS: This was a cross-sectional study conducted on 1270 workers. Data were collected using a digital questionnaire built on the KoBoToolbox platform that included the EUROHIS-QOL eight-item index to assess quality of life. Demographic, socioeconomic, behavioral, and clinical variables were considered explanatory. The associations were analyzed using the ordinal logistic regression model at a 5% significance level. RESULTS: Men and women had a mean quality of life of 31.1 and 29.4, respectively. Workers that rated their health as "very good" had an odds ratio of 7.4 (95% confidence interval (CI) = 5.17-10.81), and those who rated it as "good" had an odds ratio of 2.9 (95% CI = 2.31-3.77). Both these groups of workers were more likely to have higher levels of quality of life as compared to workers with "regular", "poor", or "very poor" self-rated health. Physically active individuals were 30% more likely to have higher levels of quality of life (odds ratio = 1.3; 95% CI = 1.08-1.65). After adjusting the model by gender, age group, marital status, socioeconomic class, self-rated health, nutritional status, and risky alcohol consumption, the odds ratio of active individuals remained stable (odds ratio = 1.3; 95% CI = 1.05-1.66). CONCLUSIONS: In the present study, self-rated health, physical activity, and gender were associated with young workers' quality of life.