Latent profile analysis of occupational burnout and its influencing factors among biosafety laboratory workers

Baojun LI; Lei DING; Jing YU; Mengjie XIA; Zhencheng LIU; Qingyue YANG; Yaoqin LU

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Latent profile analysis of occupational burnout and its influencing factors among biosafety laboratory workers

VernacularTitle:生物安全实验室职工职业倦怠潜在剖面分析及影响因素
Author: Baojun LI ¹ ; Lei DING ² ; Jing YU ³ ; Mengjie XIA ⁴ ; Zhencheng LIU ¹ ; Qingyue YANG ¹ ; Yaoqin LU ⁵
Author Information

1. School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830017, China.
2. Health Commission of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang 830001, China.
3. School of Pharmacy, Xinjiang Medical University, Urumqi, Xinjiang 830054, China.
4. Xinjiang Center for Disease Control and Prevention (Xinjiang Uygur Autonomous Region Academy of Preventive Medical Sciences, Xinjiang Uygur Autonomous Region Center for Public Health Laboratory Testing), Urumqi, Xinjiang 830002, China.
5. School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830017, China;Xinjiang Center for Disease Control and Prevention (Xinjiang Uygur Autonomous Region Academy of Preventive Medical Sciences, Xinjiang Uygur Autonomous Region Center for Public Health Laboratory Testing), Urumqi, Xinjiang 830002, China.
Publication Type:Investigation
Keywords: occupational burnout; biosafety laboratory; worker; latent profile analysis; K-means clustering
From: Journal of Environmental and Occupational Medicine 2025;42(12):1472-1479
CountryChina
Language:Chinese
Abstract: Background Staff in biosafety laboratories (BSL) are more likely to experience occupational burnout and other psychological issues due to their unique working environment and high job demands. However, current research in this field tends to focus on overall analyses, overlooking the internal differences within this group. Objective To explore latent profiles of occupational burnout among BSL workers and their influencing factors, providing a reference for targeted burnout interventions. Methods In 2022, cluster random sampling was used to select 8023 BSL workers in Xinjiang. Data were collected via a questionnaire survey, including a self-designed general information questionnaire, the Maslach Burnout Inventory-General Survey (MBI-GS), and the Effort-Reward Imbalance (ERI) scale, to analyze demographic characteristics, occupational burnout, and job stress status. After data cleaning, latent profile analysis (LPA) was applied to identify burnout profiles, followed by K-means clustering for verification. Logistic regression was used to analyze influencing factors with burnout profiles as the dependent variable. Results After excluding invalid questionnaires, 7924 valid questionnaires were retained, with an recovery rate of 98.77%. The LPA results indicated that occupational burnout among BSL staff was categorized into three latent profiles: a low burnout profile (2208 individuals, 27.86%), a moderate burnout profile (4636 individuals, 58.51%), and a high burnout profile (1080 individuals, 13.63%). A similar grouping was derived from K-means clustering analysis (28.71%, 53.37%, and 17.92%), and a Sankey diagram demonstrated significant overlap between the two classification results. Statistically significant differences were observed across the three latent profiles in all three dimensions of burnout: emotional exhaustion, cynicism, and reduced personal accomplishment. The final logistic regression model revealed that the influencing factors exhibited characteristics of partial overlap and partial differentiation. Among the common influencing factors, the shared protective factors included: age ≥50 years (OR_{moderate burnout}=0.42, OR_{high burnout}=0.45), monthly income of 4000-6000 RMB (OR_{moderate burnout}=0.83, OR_{high burnout}=0.71), good sleep quality (OR_{moderate burnout}=0.70, OR_{high burnout}=0.13) and very good sleep quality (OR_{moderate burnout}=0.51, OR_{high burnout}=0.12), and engaging in physical exercise ≥5 times per week (OR_{moderate burnout}=0.74, OR_{high burnout}=0.48). The protective effects were more pronounced for the high burnout profile. The common risk factors included: 21–30 years of service (OR_{moderate burnout}=1.38, OR_{high burnout}=3.00), ≥31 years of service (OR_{moderate burnout}=1.47, OR_{high burnout}=3.39), working in "other positions" (OR_{moderate burnout}=1.46, OR_{high burnout}=1.56), and occupational stress (ERI>1) (OR_{moderate burnout}=2.62, OR_{high burnout}=3.25), with the risk effects being more significant for the high burnout profile. Regarding differentiated influencing factors, protective factors exclusively associated with the moderate burnout profile included having an associate degree (OR=0.70) and engaging in physical exercise 3-4 times per week (OR=0.74). For the high burnout profile, exclusive protective factors included age 40-50 years (OR=0.56), while exclusive risk factors included having a senior professional title (OR=1.85), holding a managerial role (OR=1.78), a high number of night shifts per month (OR=2.66), and a very high number of night shifts per month (OR=3.93). Conclusion BSL workers exhibit three distinct latent burnout profiles. Occupational burnout requires greater attention, and targeted prevention and intervention strategies should be developed based on worker subtypes.