Safety and Health at Work

Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency (산업안전보건연구원)
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Positive Factors for Return to Work After Accidents: Health Awareness, Consultation with Doctors, and Personal Characteristics of Workers

  • Kang, Dongsuk (Department of Business Administration, College of Social Sciences, Gangneung-Wonju National University (GWNU))
  • Received : 2021.04.06
  • Accepted : 2021.10.06
  • Published : 2022.03.30
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Abstract

Background: Industrial accidents can determine the overall level and quality of the work environment in industries and companies that contribute to national economic development. Korea has transformed the country from an international aid recipient to a donor country, but it has ranked first among the Organisation for Economic Co-operation and Development member countries in the number of fatal industrial accidents. Little has been known about the policy effects in terms of the workers' insurance for their industrial accidents and rehabilitation. This study raises two research questions about the influence of workers' personal characteristics and vocational rehabilitation services on their return to workplaces. Methods: The study implements weighted logistic regression analysis using propensity score matching. This research utilizes the relevant dataset (3,924 persons) of Korea's industrial accident and insurance. Results: The findings show that the level of workers' awareness of health recovery and their counseling for rehabilitation by physicians had positive effects on their return to work. Environmental factors such as workers' job stability at the time of industrial accidents and the temporal effects of industrial accidents (e.g., the level of disability, their age) had negative impacts on their return to work. Conclusions: These findings have policy implications that the concentration of rehabilitation services for patients who have been mildly affected by industrial accidents would be effective in the short and medium term. The findings also highlight the necessity of ongoing policies about workers' vocational recovery with concrete evidence about policy impacts.

Keywords

Acknowledgement

The author appreciates the Labor Welfare Research Institute of Korea Workers Compensation & Welfare Service (KCOMWEL) for sharing the dataset. The author is also very grateful to two anonymous reviewers, the editor, and the editorial office of Safety and Health at Work (SH@W) journal for improving the author's study. Any errors or typos of this research article are the author's responsibility. This study is a revised manuscript presented at the 2020 Industrial Accident Insurance Panel Conference administered by KCOMWEL. The study does not show the tables about the results of correlation between all variables, VIF, and t-tests for PSM matching due to length limitation. If readers want to check the table, please contact the author.

References

  1. Chun HM, Munyi EN, Lee H. South Korea as an emerging donor: challenges and changes on its entering OECD/DAC. J Int Dev 2010;22(6):788-802. https://doi.org/10.1002/jid.1723
  2. Kang D, Park MJ. Competitive prospects of graduate program on the integration of ICT superiority, higher education, and international aid. Telematics and Inf 2017;34(8):1625-37. https://doi.org/10.1016/j.tele.2017.07.009
  3. Chung S, Shin S. User guide for the panel survey of industrial accident and insurance (2nd cohort 1st survey) in 2018. Labor Welfare Research Institute of Korea Workers' Compensation & Welfare Service (KCOMWEL); 2019.
  4. MOEL. Year book of industrial accident and insurance business in 2018; 2019 published by Ministry of Employment and Labor in Korea (MOEL).
  5. Chung S, Shin S. Report of basis analysis on industrial accident and insurance (2nd cohort 1st survey) in 2018. Labor Welfare Research Institute of Korea Workers' Compensation & Welfare Service (KCOMWEL); 2019.
  6. Campolieti M, Gunderson MK, Smith JA. The effect of vocational rehabilitation on the employment outcomes of disability insurance beneficiaries: new evidence from Canada. IZA J Labor Pol 2014;3(1):10. https://doi.org/10.1186/2193-9004-3-10
  7. Fadyl JK, McPherson KM, Schluter PJ, Turner-Stokes L. Factors contributing to work-ability for injured workers: literature review and comparison with available measures. Disabil Rehabil 2010;32(14):1173-83. https://doi.org/10.3109/09638281003653302
  8. Selander J, Marnetoft S-U, Bergroth A, Ekholm J. Return to work following vocational rehabilitation for neck, back and shoulder problems: risk factors reviewed. Disabil Rehabil 2002;24(14):704-12. https://doi.org/10.1080/09638280210124284
  9. Kim J-W. The effects of the person-job fit of injured workers on job satisfaction and voluntary turnover after reemployment. Kor Publ Adm Rev 2019;53(2):211-48.
  10. Lawless RM, Robbennolt JK, Ulen T. Empirical methods in law. New York: Aspen Publishers; 2010.
  11. Park E. Re-employment of injured workers after a claim closure. Kor J Soc Welfare Stud 2018;34(4):31-58.
  12. Han Km, Lee Ma. Determinants factors analysis of job retention for injured workers after return-to-work using recurrent event survival analysis. Kor J Soc Welfare Stud 2017;48(4):221-49. https://doi.org/10.16999/KASWS.2017.48.4.221
  13. Gujarati D, Porter D. Basic econometrics. 5th (International edition. Mc Graw Hill Education; 2009.
  14. Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv 2008;22(1):31-72. https://doi.org/10.1111/j.1467-6419.2007.00527.x
  15. Garrido MM, Kelley AS, Paris J, Roza K, Meier DE, Morrison RS, et al. Methods for constructing and assessing propensity scores. Health Serv Res 2014;49(5):1701-20. https://doi.org/10.1111/1475-6773.12182
  16. Garrido MM. Propensity scores: a practical method for assessing treatment effects in pain and symptom management research. J Pain Symptom Manag 2014;48(4):711-8. https://doi.org/10.1016/j.jpainsymman.2014.05.014
  17. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2002;2(4):358-77. https://doi.org/10.1177/1536867x0200200403
  18. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing; 2003.
  19. Nichols A. Causal inference with observational data. Stata J 2007;7(4):507-41. https://doi.org/10.1177/1536867x0700700403
  20. Greene WH. Econometric analysis. 7th (International edition. Pearson Education Limited; 2012.
  21. Rodrigues M, de la Riva J, Fotheringham S. Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl Geogr 2014;48:52-63. https://doi.org/10.1016/j.apgeog.2014.01.011