Validade da Escala de Estresse Percebido em estudantes universitários brasileiros de baixa renda

Autores

DOI:

https://doi.org/10.11606/s1518-8787.2025059005974

Palavras-chave:

Mental Health, Psychometrics, Adult, Income, Unsupervised Machine Learning

Resumo

OBJECTIVE: We tested the reliability and validity of the Perceived Stress Scale, an online questionnaire, among college students from low-income Brazilian regions.  METHODS: We assessed 195 college students from a region with a Gini index of 0.56 for the validity study and a subsample of 117 students for the reliability study, where we evaluated the Perceived Stress Scale with the 14 original items. We also applied the shortened version of the Brief Symptom Inventory with 18 items (BSI-18). The psychometric properties analyzed, including temporal stability, internal consistency, and structural and convergent validity, were assessed using Spearman’s correlation coefficient, Cronbach’s alpha coefficient, unsupervised machine learning, and confirmatory factor analysis.  RESULTS: The questionnaire showed acceptable reliability (temporal stability [rho ≥ 0.32] and internal consistency [alpha ≥ 0.79]). In construct validity, we identified two clusters, “helplessness” and “self-efficacy”, as structure solutions for our sample via unsupervised machine learning. An acceptable fit for the two-factor structure of the scale was indicated by multiple indices (chi-square/degrees of freedom [χ2/df] = 119/76; Tucker-Lewis Index [TLI] = 0.916; Comparative Fit Index [CFI] = 0.930; root mean square error of approximation [RMSEA] = 0.054; standardized root mean-squared residual [SRMR] = 0.078)) on confirmatory factor analysis. Moreover, convergent validity was supported by significant correlations of the BSI-18 Global Severity Index score with perception of helplessness (rho = 0.71) and self-efficacy (rho = -0.42).  CONCLUSION: The Perceived Stress Scale, which is an online tool, is a reliable and valid self-report tool for college students. 

Referências

Lee EH. Review of the psychometric evidence of the perceived stress scale. Asian Nurs Res (Korean Soc Nurs Sci). 2012 Dec;6(4):121-7. https://doi.org/10.1016/j.anr.2012.08.004.

Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983 Dec;24(4):385-96. https://doi.org/10.2307/2136404.

Reis RS, Hino AA, Añez CR. Perceived stress scale: reliability and validity study in Brazil. J Health Psychol. 2010 Jan;15(1):107-14. https://doi.org/10.1177/1359105309346343.

Luft CD, Sanches SO, Mazo GZ, Andrade A. Versão brasileira da Escala de Estresse Percebido: tradução e validação para idosos. Rev Saude Publica. 2007 Aug;41(4):606-15. https://doi.org/10.1590/S0034-89102007000400015.

Yokokura AV, Silva AA, Fernandes JK, Del-Bem CM, Figueiredo FP, Barbieri MA, et al. Perceived Stress Scale: confirmatory factor analysis of the PSS14 and PSS10 versions in two samples of pregnant women from the BRISA cohort. Cad Saude Publica. 2017;33(12):e00184615. https://doi.org/10.1590/0102-311X00184615.

Andreou E, Alexopoulos EC, Lionis C, et al. Perceived Stress Scale: reliability and validity study in Greece. Int J Environ Res Public Health. 2011;8(8):3287-98. https://doi.org/10.3390/ijerph8083287.

Huang F, Wang H, Wang Z, Zhang J, Du W, Su C, et al. Psychometric properties of the perceived stress scale in a community sample of Chinese. BMC Psychiatry. 03 20 2020;20(1):130. https://doi.org/10.1186/s12888-020-02520-4.

Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health. 2018 Jun;6:149. https://doi.org/10.3389/fpubh.2018.00149.

Baik SH, Fox RS, Mills SD, Roesch SC, Sadler GR, Klonoff EA, et al. Reliability and validity of the Perceived Stress Scale-10 in Hispanic Americans with English or Spanish language preference. J Health Psychol. 2019 Apr;24(5):628-39. https://doi.org/10.1177/1359105316684938.

Santiago PHR, Nielsen T, Smithers LG, Roberts R, Jamieson L. Measuring stress in Australia: validation of the perceived stress scale (PSS-14) in a national sample. Health Qual Life Outcomes. 2020 Apr;18(1):100. https://doi.org/10.1186/s12955-020-01343-x.

She Z, Li D, Zhang W, Zhou N, Xi J, Ju K. Three versions of the Perceived Stress Scale: psychometric evaluation in a nationally representative sample of Chinese adults during the COVID-19 pandemic. Int J Environ Res Public Health. 2021 Aug;18(16):8312. https://doi.org/10.3390/ijerph18168312.

Dias JC, Silva WR, Maroco J, Campos JA. Escala de estresse percebido aplicada a estudantes universitárias: estudo de validação. Psychol Community Health. 2015;4(1):1-13. https://doi.org/10.5964/pch.v4i1.90.

De Man J, Campbell L, Tabana H, Wouters E. The pandemic of online research in times of COVID-19. BMJ Open. 2021;11(2):e043866. https://doi.org/10.1136/bmjopen-2020-043866.

Nascimento-Ferreira MV, Marin KA, Ferreira RKA, Oliveira LF, Bandeira AC, Sousa PS, et al. 24 h movement behavior and metabolic syndrome study protocol: a prospective cohort study on lifestyle and risk of developing metabolic syndrome in undergraduate students from low-income regions during a pandemic. Front Epidemiol. 2022 Sep:1010832. https://doi.org/10.3389/fepid.2022.1010832.

Ministério da Saúde (BR). Informções de Saúde. Índice de Gini da renda domiciliar per capita – Maranhão: período: 1991, 2000 e 2010. Brasília, DF: Ministério da Saúde [cited 2022 Apr 4]. Available from: http://tabnet.datasus.gov.br/cgi/ibge/censo/cnv/ginima.def.

Instituto Brasileiro de Geografia e Estatística. Síntese de indicadores sociais. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2023 [cited 2023 June 26]. Available from: https://cidades.ibge.gov.br/brasil/ma/pesquisa/45/82120.

Martınez-Gonzalez M, Sanchez-Villegas A, Atucha E, Fajardo J. Bioestadistica amigable. 3rd ed. [S. l.]: Elsevier; 2014.

Morgado FF, Meireles JF, Neves CM, Amaral AC, Ferreira ME. Scale development: ten main limitations and recommendations to improve future research practices. Psicol Reflex Crit. 2017 Jan;30(1):3. https://doi.org/10.1186/s41155-016-0057-1.

Barbosa JB, Santos AM, Barbosa MM, Barbosa MM, Carvalho CA, Fonseca PC, et al. Metabolic syndrome, insulin resistance and other cardiovascular risk factors in university students. Cien Saude Colet. 2016 Apr;21(4):1123-36. https://doi.org/10.1590/1413-81232015214.10472015.

Derogatis L. Brief Symptoms Inventory 18: administration, scoring, and procedures manual. [S. l.]: NCS Pearson; 2001.

Franke GH, Jaeger S, Glaesmer H, Barkmann C, Petrowski K, Braehler E. Psychometric analysis of the brief symptom inventory 18 (BSI-18). in a representative German sample. BMC Med Res Methodol. 2017 Jan;17(1):14. https://doi.org/10.1186/s12874-016-0283-3.

Nazaré B, Pereira M, Canavarro M. Avaliação breve da psicossintomatologia: análise fatorial confirmatória da versão portuguesa do Brief Symptom Inventory 18 (BSI 18). Anal Psicol. 2017;35(2):213-30. https://doi.org/10.14417/ap.1287.

Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005 Jun;146(6):732-7. https://doi.org/10.1016/j.jpeds.2005.01.055.

Hair J, Black W, Babin B, Anderson R. Multivariate data analysis. [S. l.]:Prentice Hall; 2009. V. 7.

Kien C, Schultes MT, Szelag M, Schoberberger R, Gartlehner G. German language questionnaires for assessing implementation constructs and outcomes of psychosocial and health-related interventions: a systematic review. Implement Sci. 12 12 2018;13(1):150. https://doi.org/10.1186/s13012-018-0837-3.

Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 03 2019;19(1):64. https://doi.org/10.1186/s12874-019-0681-4.

Engl E, Smittenaar P, Sgaier SK. Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide. Gates Open Res. 2019 Oct;3:1503. https://doi.org/10.12688/gatesopenres.13029.2.

Edifix has not found an issue number in the journal reference. Please check the volume/issue information. (Ref. 26 “Engl, Smittenaar, Sgaier, 2019”).

StataCorp. cluster stop: cluster-analysis stopping rules. [S. l.]: StataCorp; 2015 [cited 2024 Oct 29]. Available from: https://www.stata.com/manuals/mvclusterstop.pdf.

Iqbal T, Elahi A, Wijns W, Shahzad A. Exploring unsupervised machine learning classification methods for physiological stress detection. Front Med Technol. 2022 Mar;4:782756. https://doi.org/10.3389/fmedt.2022.782756.

Hu L, Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model A Multidiscip J. 1999;6:1-55. https://doi.org/10.1080/10705519909540118.

Publicado

2025-02-28

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Seção

Artigos Originais

Como Citar

Rosa, A. C. A., Silva, L. C. C. da, Azevedo, J. C. C., Oliveira, R. T. S., Ferreira, R. K. A., Parra, M. T., Carvalho , H. B., Moraes, A. C. F. de, & Nascimento-Ferreira, M. V. (2025). Validade da Escala de Estresse Percebido em estudantes universitários brasileiros de baixa renda. Revista De Saúde Pública, 59, e235655. https://doi.org/10.11606/s1518-8787.2025059005974

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