Artificial intelligence in the analysis of emotions of nursing students undergoing clinical simulation.

Casandra Genoveva Rosales Martins Ponce de Leon, Leandro Yukio Mano, Danielle da Silva Fernandes, Rayanne Augusta Parente Paula, Guilherme da Costa Brasil, Laiane Medeiros Ribeiro
Author Information
  1. Casandra Genoveva Rosales Martins Ponce de Leon: Universidade de Brasília. Brasília, Distrito Federal, Brazil. ORCID
  2. Leandro Yukio Mano: Universidade Estadual do Rio de Janeiro. Rio de Janeiro, Rio de Janeiro, Brazil. ORCID
  3. Danielle da Silva Fernandes: Universidade de Brasília. Brasília, Distrito Federal, Brazil. ORCID
  4. Rayanne Augusta Parente Paula: Secretaria de Saúde do Distrito Federal. Brasília, Distrito Federal, Brazil. ORCID
  5. Guilherme da Costa Brasil: Centro Universitário do Distrito Federal. Brasília, Distrito Federal, Brazil. ORCID
  6. Laiane Medeiros Ribeiro: Universidade de Brasília. Brasília, Distrito Federal, Brazil. ORCID

Abstract

OBJECTIVE: to assess nursing students' emotions undergoing maternal-child clinical simulation.
METHODS: an observational study, carried out between June and July 2019. The Focus Group technique was used, with 28 nursing students, randomly distributed into three groups, with qualitative (Bardin technique) and quantitative data (Artificial Intelligence) analysis, to analyze emotions through facial expressions, tone of voice and description of speeches.
RESULTS: we defined two categories: "It was not easy, it was very stressful"; and "Very valuable experience". In Artificial Intelligence, emotional distribution between face, voice and speech revealed a prevalence of negative valence, medium-high degree of passivity, medium power to control the situation and medium-high degree of obstruction in task accomplishment.
FINAL CONSIDERATIONS: this study revealed an oscillation between positive and negative emotions, and shows to the importance of recognizing them in the teaching-learning process in mother-child simulation.

References

  1. PLoS One. 2020 Jul 30;15(7):e0236085 [PMID: 32730277]
  2. BMC Med Educ. 2019 Aug 29;19(1):323 [PMID: 31464614]
  3. Nurse Educ Pract. 2021 Jan;50:102949 [PMID: 33310511]
  4. Nurse Educ Pract. 2019 Mar;36:13-19 [PMID: 30831482]
  5. Int J Nurs Educ Scholarsh. 2019 Feb 23;16(1): [PMID: 30798326]
  6. Nurse Educ Today. 2021 Sep;104:104981 [PMID: 34062333]
  7. Nurse Educ. 2019 Mar/Apr;44(2):E6-E9 [PMID: 30052586]
  8. Cien Saude Colet. 2018 Jan;23(1):51-59 [PMID: 29267811]
  9. Enferm Clin (Engl Ed). 2020 Nov - Dec;30(6):404-410 [PMID: 31443936]
  10. JBI Database System Rev Implement Rep. 2015 Jan;13(1):14-26 [PMID: 26447004]
  11. Rev Lat Am Enfermagem. 2020 Feb 14;28:e3248 [PMID: 32074210]
  12. Eur J Gen Pract. 2018 Dec;24(1):9-18 [PMID: 29199486]
  13. Nurs Educ Perspect. 2021 Mar-Apr 01;42(2):104-106 [PMID: 32049872]
  14. J Interprof Care. 2019 Jan-Feb;33(1):57-65 [PMID: 30160542]
  15. Rev Esc Enferm USP. 2021 Aug 23;55:e20200533 [PMID: 34435613]
  16. Int J Environ Res Public Health. 2021 May 19;18(10): [PMID: 34069709]

MeSH Term

Humans
Students, Nursing
Artificial Intelligence
Learning
Focus Groups
Emotions

Word Cloud

Created with Highcharts 10.0.0emotionsnursingsimulationArtificialundergoingclinicalstudytechniquestudentsIntelligenceanalysisvoicerevealednegativemedium-highdegreeOBJECTIVE:assessstudents'maternal-childMETHODS:observationalcarriedJuneJuly2019FocusGroupused28randomlydistributedthreegroupsqualitativeBardinquantitativedataanalyzefacialexpressionstonedescriptionspeechesRESULTS:definedtwocategories:"Iteasystressful""Veryvaluableexperience"emotionaldistributionfacespeechprevalencevalencepassivitymediumpowercontrolsituationobstructiontaskaccomplishmentFINALCONSIDERATIONS:oscillationpositiveshowsimportancerecognizingteaching-learningprocessmother-childintelligence

Similar Articles

Cited By