Safety and Health at Work

Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency (산업안전보건연구원)
권호 표지
DOI QR코드

DOI QR Code

Comparison of the Association Between Presenteeism and Absenteeism among Replacement Workers and Paid Workers: Cross-sectional Studies and Machine Learning Techniques

  • Heejoo Park (Department of Public Health, Graduate School, Yonsei University) ;
  • Juho Sim (Department of Preventive Medicine, Yonsei University College of Medicine) ;
  • Juyeon Oh (Department of Public Health, Graduate School, Yonsei University) ;
  • Jongmin Lee (Department of Occupational Health, Graduate School of Public Health, Yonsei University) ;
  • Chorom Lee (Department of Occupational Health, Graduate School of Public Health, Yonsei University) ;
  • Yangwook Kim (Department of Public Health, Graduate School, Yonsei University) ;
  • Byungyoon Yun (Department of Preventive Medicine, Yonsei University College of Medicine) ;
  • Jin-ha Yoon (Department of Preventive Medicine, Yonsei University College of Medicine)
  • Received : 2023.02.26
  • Accepted : 2024.03.03
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Replacement drivers represent a significant portion of platform labor in the Republic of Korea, often facing night shifts and the demands of emotional labor. Research on replacement drivers is limited due to their widespread nature. This study examined the levels of presenteeism and absenteeism among replacement drivers in comparison to those of paid male workers in the Republic of Korea. Methods: This study collected data for replacement drivers and used data from the 6th Korean Working Conditions Survey for paid male workers over the age of 20 years. Propensity score matching was performed to balance the differences between paid workers and replacement drivers. Multivariable logistic regression was used to estimate the adjusted odds ratio (OR) and 95% confidence intervals for presenteeism and absenteeism by replacement drivers. Stratified analysis was conducted for age groups, educational levels, income levels, and working hours. The analysis was adjusted for variables including age, education, income, working hours, working days per week, and working duration. Results: Among the 1,417 participants, the prevalence of presenteeism and absenteeism among replacement drivers was 53.6% (n = 210) and 51.3% (n = 201), respectively. The association of presenteeism and absenteeism (adjusted OR [95% CI] = 8.42 [6.36-11.16] and 20.80 [95% CI = 14.60-29.62], respectively) with replacement drivers being significant, with a prominent association among the young age group, high educational, and medium income levels. Conclusion: The results demonstrated that replacement drivers were more significantly associated with presenteeism and absenteeism than paid workers. Further studies are necessary to establish a strategy to decrease the risk factors among replacement drivers.

Keywords

Acknowledgement

This study was approved by our institutional review board and was conducted in accordance with the ethical requirements of the 1975 Declaration of Helsinki and its later amendments (IRB No 4-2022-0509).

References

  1. Bajwa U, Gastaldo D, Di Ruggiero E, Knorr L. The health of workers in the global gig economy. Global Health 2018;14(1):124. https://doi.org/10.1186/s12992-018-0444-8. PMID: 30563539.
  2. Berg J, Furrer M, Harmon E, Rani U, Silberman MS. Digital labour platforms and the future of work: Towards decent work in the online world; 2018.
  3. Kim S, Kang M, Park J. Digital industrial accidents: a case study of the mental distress of platform workers in South Korea. Int J Soc Welfare 2022;31(3):355-67. https://doi.org/10.1111/ijsw.12522
  4. Cao X, Zhang D, Huang L. The impact of COVID-19 pandemic on gig economy labor supply. JNSSoB; 2020.
  5. Allon G, Cohen MC, Sinchaisri WP. The impact of behavioral and economic drivers on gig economy workers. JAaS; 2018.
  6. Jung PK, Won JU, Roh J, Lee JH, Seok H, Lee W, Yoon JH. Workplace Violence Experienced by substitute (Daeri) drivers and its relationship to depression in Korea. J Korean Med Sci 2015 Dec;30(12):1748-53. https://doi.org/10.3346/jkms.2015.30.12.1748. Epub 2015 Nov 30. PMID: 26713049; PMCID: PMC4689818.
  7. Yun J. Seoul's nocturnal urbanism: An emergent night-time economy of substitute driving and fast deliveries. Urban Stud 2022;59(6):1238-54. https://doi.org/10.1177/00420980211005963
  8. Cho SS, Lee DW, Kang MY. The association between shift work and healthrelated productivity loss due to either sickness absence or reduced performance at work: a cross-sectional study of Korea. Int J Environ Res Public Health 2020;17(22). https://doi.org/10.3390/ijerph17228493. PMID: 33207798.
  9. Sung Won J, June-Hee L, Kyung-Jae L. Assessing the association between emotional labor and presenteeism among nurses in Korea: cross-sectional study using the 4th Korean Working Conditions Survey. Saf Health Work 2020;11(1):103-8. https://doi.org/10.1016/j.shaw.2019.12.002. PMID: JUNG2020103.
  10. Sanderson K, Cocker F. Presenteeism: implications and health risks. Aust Fam Physician 2013;42(4):172-5.
  11. Grinza E, Rycx F. The impact of sickness absenteeism on firm productivity: new evidence from Belgian matched employereemployee panel data. Ind Relat J Econ Soc 2020;59(1):150-94.
  12. Bergstrom G, Bodin L, Hagberg J, Lindh T, Aronsson G, Josephson M. Does sickness presenteeism have an impact on future general health? Int Arch Occup Environ Health 2009 Nov;82(10):1179-90. https://doi.org/10.1007/s00420-009-0433-6. PMID: 19504117.
  13. Webster JJRpfUNIBEC. Diversity management in the ICT industry: challenges and issues for social dialogue; 2007.
  14. Vanni K, Neupane S, Nygard C-H. An effort to assess the relation between productivity loss costs and presenteeism at work. Int J Occup Saf Ergon 2017;23(1):33-43. https://doi.org/10.1080/10803548.2016.1197578.
  15. Ose S. Precarious work, sickness absence and risk sharing between employers, employees and social insurance: Solveig Ose. Eur J Public Health 2022;32(Suppl. 3). https://doi.org/10.1093/eurpub/ckac129.376.
  16. Julian McFarlane S, Occa A, Peng W, Awonuga O, Morgan SE. Communitybased participatory research (CBPR) to enhance participation of racial/ethnic minorities in clinical trials: a 10-year systematic review. Health Commun 2022;37(9):1075-92. https://doi.org/10.1080/10410236.2021.1943978.
  17. Leighton K, Kardong-Edgren S, Schneidereith T, Foisy-Doll C. Using social media and snowball sampling as an alternative recruitment strategy for research. Clin Simul Nurs 2021;55:37-42. https://doi.org/10.1016/j.ecns.2021.03.006.
  18. Lee NR, Kim SW, Joo JH, Lee JH, Lee JH, Lee KJ. Relationship between workplace violence and work-related depression/anxiety, separating the types of perpetrators: a cross-sectional study using data from the fourth and fifth Korean Working Conditions Surveys (KWCS). Ann Occup Environ Med 2022;34:e13. https://doi.org/10.35371/aoem.2022.34.e13. PMID: 35923792.
  19. Shin MG, Kim Y-K, Kim S-Y, Kang DM. Relationship between job training and subjective well-being in accordance with work creativity, task variety, and occupation. Saf Health Work 2020;11(4):466-78. https://doi.org/10.1016/j.shaw.2020.08.006.
  20. Kim YS, Park J, Rhee KY, Kim HM. A comparison between the second Korean Working Conditions Survey (KWCS) and the first KWCS. Saf Health Work 2015;6(2):85-9. https://doi.org/10.1016/j.shaw.2015.02.005. PMID: 26106506.
  21. Kurth T, Smorkalov M, Mendygral P, Sridharan S, Mathuriya A. TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. Concurrency Comput Pract Ex 2019;31(16):e4989.
  22. Reuter M, Wahrendorf M, Di Tecco C, Probst TM, Ruhle S, Ghezzi V, Barbaranelli C, Iavicoli S, Dragano N. Do temporary workers more often decide to work while sick? Evidence for the link between employment contract and presenteeism in Europe. Int J Environ Res Public Health 2019;16:1868. https://doi.org/10.3390/ijerph16101868.
  23. Kim JY, Lee J, Muntaner C, Kim S-S. Who is working while sick? Nonstandard employment and its association with absenteeism and presenteeism in South Korea. Int Arch Occup Environ Health 2016;89(7):1095-101. https://doi.org/10.1007/s00420-016-1146-2.
  24. Natti J, Oinas T, Harma M, Anttila T, Kandolin I. Combined effects of shiftwork and individual working time control on long-term sickness absence: a prospective study of Finnish employees. J Occup Environ Med 2014;56(7):732-8. https://doi.org/10.1097/JOM.0000000000000176
  25. Kayaba M, Sasai-Sakuma T, Takaesu Y, Inoue Y. The relationship between insomnia symptoms and work productivity among blue-collar and whitecollar Japanese workers engaged in construction/civil engineering work: a cross-sectional study. BMC Public Health 2021;21(1):1244. https://doi.org/10.1186/s12889-021-11273-y.
  26. Wee LH, Yeap LLL, Chan CMH, Wong JE, Jamil NA, Swarna Nantha Y, Siau CS. Anteceding factors predicting absenteeism and presenteeism in urban area in Malaysia. BMC Publ Health 2019;19:1e12.
  27. Taloyan M, Aronsson G, Leineweber C, Magnusson Hanson L, Alexanderson K, Westerlund H. Sickness presenteeism predicts suboptimal self-rated health and sickness absence: a nationally representative study of the Swedish working population. PLoS One 2012;7(9):e44721. https://doi.org/10.1371/journal.pone.0044721. PMID: 22984547.
  28. Susser P, Ziebarth NR. Profiling the U.S. sick leave landscape: presenteeism among females. Health Serv Res 2016;51(6):2305-17. https://doi.org/10.1111/1475-6773.12471. PMID: 26947427.
  29. Martinez LF, Ferreira AI. Sick at work: presenteeism among nurses in a Portuguese public hospital. Stress Health 2012;28(4):297-304. https://doi.org/10.1002/smi.1432. PMID: 22282226.
  30. Framke E, Sorensen JK, Alexanderson K, Farrants K, Kivimaki M, Nyberg ST. Emotional demands at work and risk of long-term sickness absence in 1$5 1$5 million employees in Denmark: a prospective cohort study on effect modifiers. Lancet Public Health 2021;6(10):e752-9. https://doi.org/10.1016/S2468-2667(21)00185-7.
  31. Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited sample size. PLoS One 2019;14(11):e0224365. https://doi.org/10.1371/journal.pone.0224365. PMID: pub.1122390471.