과제정보
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.
참고문헌
- 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.
- 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.
- 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.
- Lerouge L. Psychosocial risks in labour and social security law: a comparative legal overview from europe. Cham: North America, Australia and Japan. Springer; 2017.
- Dollard M, Dormann C, Idris M. Psychosocial safety climate: a new work stress theory. Cham: Springer; 2019.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Haykin S. Neural networks and learning machines. 3rd ed. New York: Prentice Hall; 2009.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- AABB. American. Association of Blood Banks; 2019. Available from: https://healthfinder.gov/FindServices/Organizations/Organization.aspx?code=HR0147.
- Cohen L, Manion L, Morrison K. Research methods in education. 8th ed. New York: Routledge; 2017.
- 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.
- McMillan J, Schumacher S. Research in education: evidence-based inquiry. 7th ed. New York: Prentice Hall; 2009.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.