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Psychosocial risk management

2020Conference paper Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
dc.contributor.authorFernandes, Ana
dc.contributor.authorFigueiredo, Margarida
dc.contributor.authorÁvidos, Liliana
dc.contributor.authorRibeiro, Jorge
dc.contributor.authorVicente, Dinis
dc.contributor.authorNeves, José
dc.contributor.authorVicente, Henrique
dc.date.accessioned2025-10-10T16:25:38Z
dc.date.available2025-10-10T16:25:38Z
dc.date.issued2020
dc.descriptionConference name - 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020; Conference city - Virtual Online; Conference date - 16 September 2020 - 18 September 2020; Conference code - 163593
dc.description.abstractA number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs, this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming, complemented with an Artificial Neural Network approach to computing.eng
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
dc.identifier.citationAna Fernandes, Margarida Figueiredo, Liliana Ávidos, Jorge Ribeiro, Dinis Vicente, José Neves, Henrique Vicente, Psychosocial risk management, Procedia Computer Science, Volume 176, 2020, Pages 743-752, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.09.069.
dc.identifier.doi10.1016/j.procs.2020.09.069
dc.identifier.eissn1877-0509
dc.identifier.urihttp://hdl.handle.net/10400.8/14249
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationALGORITMI Research Center
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1877050920319645?via%3Dihub
dc.relation.ispartofProcedia Computer Science
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPsychosocial Risk Management
dc.subjectEntropy
dc.subjectKnowledge Representation and Reasoning
dc.subjectLogic Programming
dc.subjectArtificial Neural Networks
dc.titlePsychosocial risk managementeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleALGORITMI Research Center
oaire.awardURIhttp://hdl.handle.net/10400.8/14247
oaire.citation.conferenceDate2009-09
oaire.citation.conferencePlaceVirtual, Online
oaire.citation.endPage752
oaire.citation.startPage743
oaire.citation.titleProcedia Computer Science
oaire.citation.volume176
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameVicente
person.givenNameDinis
person.identifier.orcid0000-0002-5856-1279
relation.isAuthorOfPublication5d972595-cb7a-47d3-a2b1-94e6c8840674
relation.isAuthorOfPublication.latestForDiscovery5d972595-cb7a-47d3-a2b1-94e6c8840674
relation.isProjectOfPublication69ebc407-cbd1-4057-9da5-59997a73bcb9
relation.isProjectOfPublication.latestForDiscovery69ebc407-cbd1-4057-9da5-59997a73bcb9

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A number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs, this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming, complemented with an Artificial Neural Network approach to computing.
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