Work-related stress has emerged as a critical issue in public health and labour policy, particularly in light of evolving work environments and the effects of the COVID-19 pandemic. This study examines the perception of work stress across European Union member states by utilising CUB models, a class of mixture models well-suited for analysing ordinal data. The data for this research stems from the Flash Eurobarometer - OSH Pulse survey, conducted by the European Commission Agency for Occupational Safety and Health (EU-OSHA) in Spring 2022. Our findings indicate that the pandemic crisis has affected workers in the EU differently, with a greater impact on self-employed individuals, women, and more educated workers, who reported higher levels of work-stress. To support this analysis, the graphical features of CUB models effectively illustrate diverse response patterns across different countries and respondent profiles. Additionally, the Gretl CUB package has been updated to allow the use of sample weights in the estimation algorithm.
FrankJMustardCSmithP, et al.Work as a social determinant of health in high-income countries: past, present, and future. The Lancet2023; 402: 1357–1367.
2.
du PrelJBBjelajacAKFranićZ, et al.The relationship between work-related stress and depression: a scoping review. Public Health Rev2024; 45: 1606968.
LeclercCDe KeulenaerFBelliS. Osh pulse-occupational safety and health in post-pandemic workplaces. Europ Agency Safety Health at Work2022; 10: 478476.
5.
Maţcu-ZahariaMHorodnicIAWilliamsCC, et al.Self-employed workers and the achievement of the 2030 agenda for sustainable development: An overview of their social benefit entitlements across 31 european countries. Sustain2024; 16: 2625.
6.
WeberTEiffeFFAdăscăliţeiD. Self-employment in the EU: Job quality and developments in social protection. Luxembourg: Publications Office of the European Union, 2024.
7.
CaliendoMGraeberDKritikosAS, et al.Pandemic depression: COVID-19 and the mental health of the self-employed. Entrepreneurs Theory Pract2023; 47: 788–830.
8.
SpasovaSRoshanS. Self-employment and social protection in europe: improved protection amid persistent gaps and fragmentation. In: Research handbook on self-employment and public policy, 2024. pp.296–315. Edward Elgar Publishing.
9.
DavidATerstriepJSchäferS, et al.An intersectional perspective on the impacts and responses of entrepreneurs during the COVID-19 pandemic in germany. The Int J Entrepreneurs Innov2024; 14657503241258933.
10.
LeeSHPatelPCPhanPH. Are the self-employed more stressed? new evidence on an old question. J Small Busin Manage2023; 61: 513–539.
11.
KieflSFischerSSchmittJ. Self-employed and stressed out? the impact of stress and stress management on entrepreneurs’ mental health and performance. Front Psychol2024; 15: 1365489.
12.
StephanUZbierowskiPPérez-LuñoA, et al.Act or wait-and-see? adversity, agility, and entrepreneur wellbeing across countries during the COVID-19 pandemic. Entrepreneurs Theory Pract2023; 47: 682–723.
13.
GerogiannisICapecchiSCurtarelliM. Mental health and the COVID-19 pandemic in Europe: a focus on the self-employed. BRQ Business Res Quart2025; 28: 695–711.
14.
PiccoloDSimoneR. The class of cub models: statistical foundations, inferential issues and empirical evidence. Stat Methods Appt2019; 28: 389–435.
15.
EU-OSHA. Osh pulse - occupational safety and health in post-pandemic workplaces (flash eurobarometer). (ZA8753; Version 2.0.0) [Data set]. GESIS, Cologne. https://doi.org/10.4232/1.14208, 2023.
16.
EU-OSHA. Osh pulse - occupational safety and health in post-pandemic workplaces (flash eurobarometer). (ZA8753; Version 2.0.0) [technical documents]. GESIS, Cologne. https://doi.org/10.4232/1.14208, 2023. DOI: 10.4232/1.14208.
17.
McCullaghPNelderJ. Generalized linear models. 2nd ed. Boca Raton: Chapman and Hall/CRC, 1989.
18.
AgrestiA. Analysis of ordinal categorical data. Hoboken: Wiley, 2010.
19.
TutzG. Regression for categorical data, Vol. 34. New York: Cambridge University Press, 2011.
20.
PiccoloD. On the moments of a mixture of uniform and shifted binomial random variables. Quad Stat2003; 5: 85–104.
21.
PiccoloDSimoneRIannarioM. Cumulative and cub models for rating data: a comparative analysis. Int Stat Rev2019; 87: 207–236.
22.
McLachlanGKrishnanT. The EM Algorithm and Extensions. Vol. 656. 2nd ed. Wiley Series in Probability and Statistics, Hoboken, New Jersey, 2008.
23.
DempsterALairdNRubinD. Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B1977; 39: 1–38.
24.
SimoneR.Di IorioF.LucchettiR.. CUB for Gretl. In: Di IorioF.LucchettiR. (eds) Proceedings of the International Conference on the GNU Regression, Econometrics and Time Series Library. Napoli: feDOA University Press, 2019, pp.147–166.
CerulliGSimoneRDi IorioF, et al.Fitting mixture models for feeling and uncertainty for rating data analysis. Stata J2022; 22: 195–223.
27.
CapecchiSGambacortaRSimoneR, et al.Modelling cognitive response patterns to survey questions using the class of cub models. Technical report, Bank of Italy, Quanderni di economia e finanza, n.885, 2024.
28.
KolenikovS. Resampling variance estimation for complex survey data. Stata J2010; 10: 165–199.
EverittBLandauSLeeseM, et al.Cluster analysis. Chichester: Wiley, 2011.
31.
MensahA. Job stress and mental well-being among working men and women in europe: The mediating role of social support. Int J Environ Res Public Health2021; 18: 2494.
32.
European Commission. Employment and Social Developments in Europe 2024. Upward social convergence in the EU and the role of social investment. Luxembourg: Publications Office of the European Union, 2024. DOI: 10.2767/837399.
33.
RigóMDraganoNWahrendorfM, et al.Work stress on rise? comparative analysis of trends in work stressors using the european working conditions survey. Int Arch Occup Environ Health2021; 94: 459–474.
34.
SchogerLI. Coping with work-related stressors: does education reduce work-related stress?. J Public Health (Bangkok)2025; 33: 1123–1134.
35.
CollettGKorszunAGuptaAK. Potential strategies for supporting mental health and mitigating the risk of burnout among healthcare professionals: insights from the COVID-19 pandemic. EClinicalMedicine2024; 71: 1–8.
36.
Ozamiz-EtxebarriaNIdoiaga MondragonNBueno-NotivolJ, et al.Prevalence of anxiety, depression, and stress among teachers during the COVID-19 pandemic: A rapid systematic review with meta-analysis. Brain Sci2021; 11: 1172.
37.
Ghali-ZinoubiZAmariAJaouaF. E-learning in era of COVID-19 pandemic: Impact of flexible working arrangements on work pressure, work-life conflict and academics’ satisfaction. Vision: The Journal of Business Perspective 2024; 28: 621–632.
38.
GraeberDKritikosASSeebauerJ. COVID-19: a crisis of the female self-employed. J Popul Econ2021; 34: 1141–1187.
39.
BirhanuAGGetachewYSLashitewAA. Gender differences in enterprise performance during the COVID-19 crisis: Do public policy responses matter?Entrepreneur Theory Pract2022; 46: 1374–1401.
40.
BriegerSASonbolDDe ClercqD. Gender differences in entrepreneurs’ work–family conflict and well-being during COVID-19: Moderating effects of gender-egalitarian contexts. J Small Business Manage2024; 62: 2322–2363.
41.
SinhaS. Multiple roles of working women and psychological well-being. Ind Psychiatry J2017; 26: 171–177.
42.
AnninkADen DulkLSteijnB. Work–family conflict among employees and the self-employed across europe. Soc Indic Res2016; 126: 571–593.
43.
NordenmarkMAlménNVinbergS. Work/family conflict of more importance than psychosocial working conditions and family conditions for mental wellbeing. Societies2020; 10: 67.
44.
MolariusAMetsiniA. The association between time spent in domestic work and mental health among women and men. Int J Environ Res Public Health2023; 20: 4948.
45.
DassaCLambertJBlaisR, et al.Effects of a neutral answer choice on the reliability and validity of attitude and opinion items. Canad J Program Evalu1997; 12: 61–80.
46.
KankarašMCapecchiS. Neither agree nor disagree: use and misuse of the neutral response category in likert-type scales. Metron2025; 83: 111–140.
