Safety and Health at Work

Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency (산업안전보건연구원)
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Psychosocial Risks Assessment in Cryopreservation Laboratories

  • Fernandes, Ana (Departamento de Quimica, Escola de Ciencias e Tecnologia, Universidade de Evora) ;
  • Figueiredo, Margarida (Departamento de Quimica, Escola de Ciencias e Tecnologia, Universidade de Evora) ;
  • Ribeiro, Jorge (Instituto Politecnico de Viana Do Castelo, Rua da Escola Industrial e Comercial de Nun'Alvares) ;
  • Neves, Jose (Centro Algoritmi, Universidade do Minho) ;
  • Vicente, Henrique (Departamento de Quimica, Escola de Ciencias e Tecnologia, Universidade de Evora)
  • Received : 2020.03.05
  • Accepted : 2020.07.07
  • Published : 2020.12.30
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Abstract

Background: Psychosocial risks are increasingly a type of risk analyzed in organizations beyond chemical, physical, and biological risks. To this type of risk, a greater attention has been given following the update of ISO 9001: 2015, more precisely the requirement 7.1.4 for the process operation environment. The update of this normative reference was intended to approximate OHSAS 18001: 2007 reference updated in 2018 with the publication of ISO 45001. Thus, the organizations are increasingly committed to achieving and demonstrating good occupational health and safety performance. Methods: The aim of this study was to characterize the psychosocial risks in a cryopreservation laboratory and to develop a predictive model for psychosocial risk management. The methodology followed to collect the information was the inquiry by questionnaire that was applied to a sample comprising 200 employees. Results: The results show that most of the respondents are aware of the psychosocial risks, identifying interpersonal relationships and emotional feelings as the main factors that lead to this type of risks. Furthermore, terms such as lack of resources, working hours, lab equipment, stress, and precariousness show strong correlation with psychosocial risks. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the psychosocial risks. Conclusion: This work presents the development of an intelligent system that allows identifying the weaknesses of the organization and contributing to the enhancement of the psychosocial risks management.

Keywords

Acknowledgement

This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/50006/2020.

References

  1. EU-OSHA. Management of psychosocial risks at work: an analysis of findings of the European survey of enterprises on new and emerging risks. Luxembourg City: Publications Office of the European Union. 2012. Available from: https://osha.europa.eu/en/publications/management-psychosocialrisks-work-analysis-findings-european-survey-enterprises-new/view.
  2. EU-OSHA. Expert forecast on emerging psychosocial risks related to occupational safety and health. Luxembourg City: Office for Official Publications of the European Communities. 2007. Available from: https://osha.europa.eu/en/publications/report-expert-forecast-emerging-psychosocial-risks-relatedoccupational-safety-and.
  3. Leka S, Jain A. Health impact of psychosocial hazards at work: an overview. Geneva: World Health Organization. 2010. Available from: https://apps.who.int/iris/bitstream/handle/10665/44428/9789241500272_eng.pdf.
  4. Lerouge L. Psychosocial risks in labour and social security law: a comparative legal overview from europe. Cham: North America, Australia and Japan. Springer; 2017.
  5. Dollard M, Dormann C, Idris M. Psychosocial safety climate: a new work stress theory. Cham: Springer; 2019.
  6. Eurofound., EU-OSHA. Psychosocial risks in europe: prevalence and strategies for prevention - a joint report from the European foundation for the improvement of living and working conditions and the European agency for safety and health at work. Luxembourg City: Publications Office of the European Union; 2014. Available from:https://osha.europa.eu/en/publications/reports/psychosocial-risks-eu-prevalence-strategies-prevention/view.
  7. Fergunson S, Alrread W, Burr D, Heaney C, Marras W. Biomechanical, psychosocial and individual risk factors predicting low back functional impairment among furniture distribution. Clin Biomech 2012;27:117-23. https://doi.org/10.1016/j.clinbiomech.2011.09.002.
  8. Joensuu M, Vaananen A, Koskinen A, Kivimaki M, Virtanen M, Vahtera J. Psychosocial work environment and hospital admissions due to mental disorders: a 15-year prospective study of industrial employees. J Affect Disord 2010;124:118-25. https://doi.org/10.1016/j.jad.2009.10.025.
  9. Metzler Y, Groeling-Muller G, Bellingrath S. Better safe than sorry: methods for risk assessment of psychosocial hazards. Saf Sci 2019;114:122-39. https://doi.org/10.1016/j.ssci.2019.01.003.
  10. Bronkhorst B. Behaving safely under pressure: the effects of job demands, resources, and safety climate on employee physical and psychosocial safety behavior. J Saf Res. 2015;55:63-72. https://doi.org/10.1016/j.jsr.2015.09.002.
  11. Bernal D, Campos-Serna J, Tobias A, Vargas-Prada S, Benavides F, Serra C. Work-related psychosocial risk factors and musculoskeletal disorders in hospital nurses and nursing aides: a systematic review and meta-analysis. Int J Nurs Stud 2015;52:635-48. https://doi.org/10.1016/j.ijnurstu.2014.11.003.
  12. Haykin S. Neural networks and learning machines. 3rd ed. New York: Prentice Hall; 2009.
  13. Mitra S, Pal S, Mitra P. Data mining in soft computing framework: a survey. IEEE Trans Neural Netw 2002;13:3-14. https://doi.org/10.1109/72.977258.
  14. Allawi MF, Jaafar O, Hamzah FM, Abdullah SM, El-shafie A. Environ Sci Pollut Res 2018;25:13446-69. https://doi.org/10.1007/s11356-018-1867-8.
  15. Ruben GB, Zhang K, Bao H, Mao X. Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water Resour Manage 2018;32:273-83. https://doi.org/10.1007/s11269-017-1809-0.
  16. Neves J, Vicente H, Esteves M, Ferraz F, Abelha A, Machado J, Machado J, Neves J, Ribeiro J, Sampaio L. A deep-big data approach to health care in the AI age. Mobile Netw Appl 2018;23:1123-8. https://doi.org/10.1007/s11036-018-1071-6.
  17. Vicente H, Martins MR, Mendes T, Vilhena J, Graneda J, Gusmao R, Neves J. A soft computing approach to acute coronary syndrome risk evaluation. Austin J Clin Cardiolog 2016;3. Article ID 1044, 8. Available from: https://austinpublishinggroup.com/clinical-cardiology/fulltext/ajcc-v3-id1044.php.
  18. Neves J, Zeleznikow J, Vicente H. Quality of judgment assessment. In: Novais P, Carneiro D, editors. Interdisciplinary perspectives on contemporary conflict resolution, advances in linguistic and communication studies. Hershey: IGI Global; 2016. p. 318-62. https://doi.org/10.4018/978-1-5225-0245-6.ch006.
  19. Nguyen TS, Nguyen LM, Tojo S, Satoh K, Shimazu A. Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts. Artif Intell Law 2018;26:169-99. https://doi.org/10.1007/s10506-018-9225-1.
  20. Stranieri A, Zeleznikow J. Insights from jurisprudence for machine learning in law. In: Kulkarni S, editor. Machine learning algorithms for problem solving in computational applications: intelligent techniques. Hershey: IGI Global; 2012. p. 85-98. https://doi.org/10.4018/978-1-4666-1833-6.ch006.
  21. Portuguese Ministry of Health. Law 12/2009 of 26 March 2009, vol. 60. Diario da Republica - 1.a serie. 2009. p. 1876-97. Available from: https://dre.pt/application/file/a/603284.
  22. AABB. American. Association of Blood Banks; 2019. Available from: https://healthfinder.gov/FindServices/Organizations/Organization.aspx?code=HR0147.
  23. Cohen L, Manion L, Morrison K. Research methods in education. 8th ed. New York: Routledge; 2017.
  24. DeKetele J, Roegiers X. Methodologie du Recueil d'Informations: fondements des Methodes d'Observation, de Questionnaire, d'Interview et d'Etudes de documents. 5th ed. Paris: DeBoeck Universite; 2016.
  25. McMillan J, Schumacher S. Research in education: evidence-based inquiry. 7th ed. New York: Prentice Hall; 2009.
  26. Bell J. Doing your research project: a guide for first-time researchers in education, health and social science. 5th ed. Maidenhead: Open University Press; 2010.
  27. Fernandes A, Vicente H, Figueiredo M, Neves M, Neves J. An evaluative model to assess the organizational efficiency in training corporations. In: Dang T, Wagner R, Kung J, Thoai N, Takizawa M, Neuhold E, editors. Future data and security engineering, lecture notes in computer science, vol. 10018. Cham: Springer; 2016. p. 415-28. https://doi.org/10.1007/978-3-319-48057-2_29.
  28. Frank E, Hall M, Witten IH. The WEKA workbench. Online appendix for "data mining: practical machine learning tools and techniques" Morgan Kaufmann. 4th ed.; 2016. Available from: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf.
  29. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explor 2009;11:10-8.
  30. Souza J, Matwin S, Japkowicz N. Evaluating data mining models: a pattern language. In: Proceedings of 9th conference on pattern language of programs. USA: Illinois. 2002. p. 1-23. Available from: https://hillside.net/plop/plop2002/final/PLoP2002_jtsouza0_1.pdf.
  31. Ando S, Ono Y, Shimaoka M, Hiruta S, Hattori Y, Hori F, Takeuchi Y. Associations of self estimated workloads with musculoskeletal symptoms among hospital nurses. Occup. Environ Med 2000;57:211-6. https://doi.org/10.1136/oem.57.3.211.
  32. MacDonald LA, Karasek R, Punnett L, Scharf T. Covariation between workplace physical and psychosocial stressors: evidence and implications for occupational health research and prevention. Ergonomics 2001;51:696-718. https://doi.org/10.1080/00140130119943.
  33. Florkowski C. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin Biochem 2008;29(Suppl. 1):S83-7.
  34. Vilhena J, Vicente H, Martins MR, Graneda J, Caldeira F, Gusmao R, Neves J, Neves J. A case-based reasoning view of thrombophilia risk. J Biomed Inform 2016;62:265-75. https://doi.org/10.1016/j.jbi.2016.07.013.