The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study

Autores
García Dilba, Diana; Castro Solano, Alejandro
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Objectives: The high-fivemodel (HFM) categorizes fivepositive human characteristics—erudition, peace, joviality, honesty, and tenacity — on the basis of an inductive psycholexical approach. This study aimed to evaluate the predictive power of the HFM in a sample of university students to assess whether these traits could predict optimal functioning after 20 months. Optimal functioning was definedas high academic performance; emotional, personal, and social well-being; and low levels of psychopathology. Methods: The study included 136 university students with a mean age of 30.8 years (SD = 1.86; 83% female; 17% male). The predictor variables were high factors (HFI – High Five Inventory –), and the criterion variables were emotional, personal, and social well-being (Mental Health Continuum-Short Form – MHC-SF –), psychopathological symptoms (Symptom Checklist-27, SCL-27), and academic achievement (self-reported academic grades). The data were analyzed both cross-sectionally and longitudinally. Results: The findingsindicated that the HFI, particularly the factors of Honesty (β = .64, p = .05) and Tenacity (β = .49, p = .01), demonstrated high predictive power in identifying profilesof optimal functioning (high complete mental well-being) in university students after 20 months. The predictive power of the model was greater when predictors and criteria were analyzed cross-sectionally rather than longitudinally (explaining 35% vs. 17% of the variance). Conclusions: HFM effectively distinguished between high- and low-complete mental well-being groups both cross-sectionally and longitudinally, demonstrating its robustness in predicting optimal functioning at various time points.
Fil: García Dilba, Diana. Universidad de Palermo; Argentina
Fil: Castro Solano, Alejandro. Universidad de Palermo; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
high factors
high five model
academic performance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/290198
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/290198
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal StudyGarcía Dilba, DianaCastro Solano, Alejandrohigh factorshigh five modelacademic performancehttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Objectives: The high-fivemodel (HFM) categorizes fivepositive human characteristics—erudition, peace, joviality, honesty, and tenacity — on the basis of an inductive psycholexical approach. This study aimed to evaluate the predictive power of the HFM in a sample of university students to assess whether these traits could predict optimal functioning after 20 months. Optimal functioning was definedas high academic performance; emotional, personal, and social well-being; and low levels of psychopathology. Methods: The study included 136 university students with a mean age of 30.8 years (SD = 1.86; 83% female; 17% male). The predictor variables were high factors (HFI – High Five Inventory –), and the criterion variables were emotional, personal, and social well-being (Mental Health Continuum-Short Form – MHC-SF –), psychopathological symptoms (Symptom Checklist-27, SCL-27), and academic achievement (self-reported academic grades). The data were analyzed both cross-sectionally and longitudinally. Results: The findingsindicated that the HFI, particularly the factors of Honesty (β = .64, p = .05) and Tenacity (β = .49, p = .01), demonstrated high predictive power in identifying profilesof optimal functioning (high complete mental well-being) in university students after 20 months. The predictive power of the model was greater when predictors and criteria were analyzed cross-sectionally rather than longitudinally (explaining 35% vs. 17% of the variance). Conclusions: HFM effectively distinguished between high- and low-complete mental well-being groups both cross-sectionally and longitudinally, demonstrating its robustness in predicting optimal functioning at various time points.Fil: García Dilba, Diana. Universidad de Palermo; ArgentinaFil: Castro Solano, Alejandro. Universidad de Palermo; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaPsychOpen2026-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/290198García Dilba, Diana; Castro Solano, Alejandro; The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study; PsychOpen; Europe's Journal of Psychology; 22; 2; 5-2026; 175-1931841-0413CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.5964/ejop.15589info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:34:32Zoai:ri.conicet.gov.ar:11336/290198instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:34:32.501CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
title The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
spellingShingle The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
García Dilba, Diana
high factors
high five model
academic performance
title_short The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
title_full The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
title_fullStr The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
title_full_unstemmed The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
title_sort The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study
dc.creator.none.fl_str_mv García Dilba, Diana
Castro Solano, Alejandro
author García Dilba, Diana
author_facet García Dilba, Diana
Castro Solano, Alejandro
author_role author
author2 Castro Solano, Alejandro
author2_role author
dc.subject.none.fl_str_mv high factors
high five model
academic performance
topic high factors
high five model
academic performance
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Objectives: The high-fivemodel (HFM) categorizes fivepositive human characteristics—erudition, peace, joviality, honesty, and tenacity — on the basis of an inductive psycholexical approach. This study aimed to evaluate the predictive power of the HFM in a sample of university students to assess whether these traits could predict optimal functioning after 20 months. Optimal functioning was definedas high academic performance; emotional, personal, and social well-being; and low levels of psychopathology. Methods: The study included 136 university students with a mean age of 30.8 years (SD = 1.86; 83% female; 17% male). The predictor variables were high factors (HFI – High Five Inventory –), and the criterion variables were emotional, personal, and social well-being (Mental Health Continuum-Short Form – MHC-SF –), psychopathological symptoms (Symptom Checklist-27, SCL-27), and academic achievement (self-reported academic grades). The data were analyzed both cross-sectionally and longitudinally. Results: The findingsindicated that the HFI, particularly the factors of Honesty (β = .64, p = .05) and Tenacity (β = .49, p = .01), demonstrated high predictive power in identifying profilesof optimal functioning (high complete mental well-being) in university students after 20 months. The predictive power of the model was greater when predictors and criteria were analyzed cross-sectionally rather than longitudinally (explaining 35% vs. 17% of the variance). Conclusions: HFM effectively distinguished between high- and low-complete mental well-being groups both cross-sectionally and longitudinally, demonstrating its robustness in predicting optimal functioning at various time points.
Fil: García Dilba, Diana. Universidad de Palermo; Argentina
Fil: Castro Solano, Alejandro. Universidad de Palermo; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Objectives: The high-fivemodel (HFM) categorizes fivepositive human characteristics—erudition, peace, joviality, honesty, and tenacity — on the basis of an inductive psycholexical approach. This study aimed to evaluate the predictive power of the HFM in a sample of university students to assess whether these traits could predict optimal functioning after 20 months. Optimal functioning was definedas high academic performance; emotional, personal, and social well-being; and low levels of psychopathology. Methods: The study included 136 university students with a mean age of 30.8 years (SD = 1.86; 83% female; 17% male). The predictor variables were high factors (HFI – High Five Inventory –), and the criterion variables were emotional, personal, and social well-being (Mental Health Continuum-Short Form – MHC-SF –), psychopathological symptoms (Symptom Checklist-27, SCL-27), and academic achievement (self-reported academic grades). The data were analyzed both cross-sectionally and longitudinally. Results: The findingsindicated that the HFI, particularly the factors of Honesty (β = .64, p = .05) and Tenacity (β = .49, p = .01), demonstrated high predictive power in identifying profilesof optimal functioning (high complete mental well-being) in university students after 20 months. The predictive power of the model was greater when predictors and criteria were analyzed cross-sectionally rather than longitudinally (explaining 35% vs. 17% of the variance). Conclusions: HFM effectively distinguished between high- and low-complete mental well-being groups both cross-sectionally and longitudinally, demonstrating its robustness in predicting optimal functioning at various time points.
publishDate 2026
dc.date.none.fl_str_mv 2026-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/290198
García Dilba, Diana; Castro Solano, Alejandro; The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study; PsychOpen; Europe's Journal of Psychology; 22; 2; 5-2026; 175-193
1841-0413
CONICET Digital
CONICET
url http://hdl.handle.net/11336/290198
identifier_str_mv García Dilba, Diana; Castro Solano, Alejandro; The High Five Model as a Predictor of Optimal Functioning in University Students: A Longitudinal Study; PsychOpen; Europe's Journal of Psychology; 22; 2; 5-2026; 175-193
1841-0413
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.5964/ejop.15589
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv PsychOpen
publisher.none.fl_str_mv PsychOpen
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.265058

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