The development and evaluation of the worker-occupation fit inventory.

BACKGROUND: Person-environment fit (PEF) theory, one of the foundational theories of occupational stress, has primarily found applications in organizational behavior and human resource management. Given the alignment between the definition of occupational stress and the essence of PEF, we introduced the concept of worker-occupation fit (WOF). To validate our theoretical model, the development of an instrument to measure WOF becomes imperative. METHODS: The Worker-Occupation Fit Inventory (WOFI) comprises three dimensions: personal trait fit (PTF), need-supply fit (NSF) and demand-ability fit (DAF). Job-related mental disorders (JRMDs) were assessed using the DASS-21. During the pre-investigation, items of the WOFI underwent screening through classic test theory (CTT) analysis. In the formal investigation, item response theory (IRT) analysis was employed to evaluate the selected items. The relationship between WOF and JRMD was verified by Pearson's correlation analysis and multiple logistic regression analysis. RESULTS: The initial version consisted of 26 items. Three common factors were extracted by exploratory factor analysis (EFA): 6 items were included in the PTF, 6 items were included in the NSF, 4 items were included in the DAF, and 10 items were deleted because of unacceptable factor loadings. The confirmatory factor analysis (CFA) verified the structure of the WOFI with χ2/df = 1.822, CFI = 0.947, and SRMSR = 0.056. The Cronbach's alpha coefficients of the PTF, NSF, and DAF were 0.91, 0.92, and 0.80, respectively. In IRT analysis, the discrimination values of all items ranged from 1.25 to 2.53, and the difficulty values of all items ranged from -6.28 to 1.30 (with no difficulty of reversal). The WOF was negatively related to job-related stress (r = -0.34, p<0.001), anxiety (r = -0.37, p<0.001), and depression (r = -0.41, p<0.001). The multiple logistic regression analysis suggested that a high level of WOF was a protective factor against job-related mental disorders, with ORs all less than 1 (p<0.001), and a low level of WOF was a risk factor for job-related mental disorders, with ORs all more than 1.0 (p<0.001). CONCLUSIONS: The results of CTT and IRT analysis indicated that the WOFI exhibits reliability and validation. The WOF effectively predicted job-related mental disorders. Subsequent studies will delve into the influence of WOFI on diverse professions and various health outcomes.

The Mediating Role of Worker-Occupation Fit in the Relationship Between Occupational Stress and Depression Symptoms in 1988 Medical Workers: A Cross-Sectional Study.

Objective: Occupational stress is generally acknowledged as a global phenomenon with significant health and economic consequences. The medical worker is a vulnerable group at a high-level risk for depression symptoms. This study aimed to examine the mediating effect of worker-occupation fit (WOF) in relation to occupational stress and depression symptoms among 1988 medical workers in China. Methods: A multi-center cross-sectional study was conducted during June and October 2020 in Henan Province, China. The participants were medical workers from four targeted hospitals (included one general and three specialized hospitals). The Depression, Anxiety, and Stress Scale (DASS-21 Scale), Worker-Occupation Fit Inventory (WOFI), as well as questions about demographic and occupational information were administered in questionnaires distributed to 1988 medical workers. Hierarchical linear regression analysis was used to examine the mediating role of worker occupation fit. Results: In this study, there are 43.5% (n = 864) of medical workers experienced depression symptoms. The mean score of WOF was 31.6 ± 7.1, characteristic fit, need supply fit and demand ability fit were 11.3 ± 2.5, 10.1 ± 2.7, 12.9 ± 2.2, respectively. The occupational stress was negatively related to worker occupation fit (r = -0.395, P < 0.001), characteristic fit (r = -0.529, P < 0.001), need supply fit (r = -0.500, P < 0.001), and demand ability fit (r = -0.345, P < 0.001). The occupational stress and depression symptoms have a positive relationship (r = 0.798, P < 0.001). The proportion of worker occupation fit mediation was 6.5% of total effect for depression symptoms. Conclusion: Occupational stress has been identified as a risk factor for depression symptoms. Practical strategies for improving medical workers' WOF level would help them better cope with various work-related stressors to reduce depression symptoms. Hospital administrators could reduce medical workers' depression symptoms by taking comprehensive measures to improve the WOF.

Identifying the risk features for occupational stress in medical workers: a cross-sectional study.

OBJECTIVE: Occupational stress is considered a worldwide epidemic experienced by a large proportion of the working population. The identification of characteristics that place people at high risk for occupational stress is the basis of managing and intervening in this condition. In this study, we aimed to identify and validate the risk features for occupational stress among medical workers using a risk model and nomogram. METHODS: This cross-sectional study included 1988 eligible participants from Henan Province in China. Occupational stress and worker-occupation fit were measured with the Depression, Anxiety and Stress Scales (DASS-21) and Worker-Occupation Fit Inventory (WOFI). The identification of risk features was achieved through constructing multiple logistic regression model, and the risk features were used to develop the risk model and nomogram. Receiver operating characteristic (ROC) curves and calibration plots were generated to assess the effectiveness and calibration of the risk model. RESULTS: Among 1988 participants in our study, there were 42.5% (845/1988) medical workers experienced occupational stress. The risk features for occupational stress included poor work-occupation fit (WOF score < 25, expected risk: 77.3%), nurse population (expected risk: 63.1%), male sex (expected risk: 67.2%), work experience duration of 11-19 years (expected risk: 54.5%), experience of a traumatic event (expected risk: 65.3%) and the lack of a regular exercise habit (expected risk: 60.2%). For medical workers who have these risk features, the expected risk probability of occupational stress would be 90.2%. CONCLUSION: The current data can be used to identify medical workers at risk of developing occupational stress. Identifying risk features for occupational stress and the work-occupation fit can support hierarchical stress management in hospitals.

A risk model and nomogram for high-frequency hearing loss in noise-exposed workers.

BACKGROUND: High-frequency hearing loss is a significant occupational health concern in many countries, and early identification can be effective for preventing hearing loss. The study aims to construct and validate a risk model for HFHL, and develop a nomogram for predicting the individual risk in noise-exposed workers. METHODS: The current research used archival data from the National Key Occupational Diseases Survey-Sichuan conducted in China from 2014 to 2017. A total of 32,121 noise-exposed workers completed the survey, of whom 80% workers (n = 25,732) comprised the training cohort for risk model development and 20% workers (n = 6389) constituted the validation cohort for model validation. The risk model and nomogram were constructed using binary logistic models. The effectiveness and calibration of the model were evaluated with the receiver operating characteristic curve and calibration plots, respectively. RESULTS: A total of 10.06% of noise-exposed workers had HFHL. Age (OR = 1.09, 95% CI: 1.083-1.104), male sex (OR = 3.25, 95% CI: 2.85-3.702), noise exposure duration (NED) (OR = 1.15, 95% CI: 1.093-1.201), and a history of working in manufacturing (OR = 1.50, 95% CI: 1.314-1.713), construction (OR = 2.29, 95% CI: 1.531-3.421), mining (OR = 2.63, 95% CI: 2.238-3.081), or for a private-owned enterprise (POE) (OR = 1.33, 95% CI: 1.202-1.476) were associated with an increased risk of HFHL (P < 0.05). CONCLUSIONS: The risk model and nomogram for HFHL can be used in application-oriented research on the prevention and management of HFHL in workplaces with high levels of noise exposure.