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    Vocational guidance and personality traits in adolescents: a systematic review of broad personality models and RIASEC associations with vocational outcomes
    (Frontiers Media SA, 2026-06-25)
    Faz Tapia , Andrea Nicole  
    ;
    Jaramillo-Cumbicus, Kayla
    ;
    Faz-Cevallos, Eduardo
    Background The associations between broad personality traits, as captured by the Big Five and related taxonomies and vocational outcomes (career maturity, career decision-making self-efficacy, vocational identity, vocational interests, and the congruence between RIASEC vocational personality and career aspirations) constitute an important field of inquiry for understanding adolescents' career development. Objective The aim of this systematic review was to synthesize the available scientific evidence (2020-2025) on these associations in adolescents, and to examine, where reported, the congruence between RIASEC vocational personality profiles and career aspirations. Methods The review followed the PRISMA 2020 guidelines. Searches were conducted in four databases (PubMed, Scopus, EBSCO, and ERIC) using three complementary search strings, in addition to a manual search. Inclusion criteria comprised: empirical studies of adolescents aged 12-18 years (or with a mean age within this range), published between 2020 and 2025 in English or Spanish, that explicitly examined associations between personality traits assessed under the Big Five model, the HEXACO model (which shares four broad factors with the Big Five), or Holland's RIASEC vocational interests model, and at least one vocational variable (career decision-making, vocational identity, vocational interests, career choice, or career maturity). From a total of 242 records identified, 14 studies were included in the qualitative synthesis. Results Conscientiousness and openness to experience are positive correlates of career maturity, career decision-making self-efficacy, and vocational identity in several, though not all, of the included studies; the strength and direction of the conscientiousness-outcome association varied across vocational outcomes and depended on which other predictors (e.g., RIASEC interests, self-efficacy) were modeled. Neuroticism (and its HEXACO counterpart, Emotionality) was consistently associated with greater decisional difficulties and lower vocational commitment. Studies that applied Holland's RIASEC model to assess vocational interests yielded mixed evidence on cross-cultural congruence: in the small number of studies available (one per cultural context), moderate congruence was observed in a collectivistic Asian sample, while low congruence was reported in a rural Latin American context. These context-specific patterns should be interpreted as preliminary, given that they rest on a single study per cultural setting. Discussion Significant gaps were identified in longitudinal studies, personality-based interventions, and evidence from Latin America. These findings underscore the value of integrating personality trait assessment into vocational guidance programs for adolescents, while acknowledging that the contribution of any single trait depends on contextual and outcome-specific factors. Systematic review registration: https://osf.io/zmdks , identifier 10.17605/OSF.IO/ZMDKS.
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    Beyond Linear Statistics: A Machine Learning Ecosystem for Early Screening of School Bullying
    (2026)
    Espinosa Pinos, Carlos Alberto  
    ;
    Paúl Bladimir Acosta-Pérez
    ;
    Aitor Larzabal-Fernández
    ;
    Francisco Sebastián Vaca-Pinto
    This study developed and validated a Machine Learning (ML) ecosystem for the early screening of school victimization among Ecuadorian adolescents, a phenomenon that poses a critical barrier to educational equity. Addressing previous methodological limitations, this research intentionally eliminated circular reasoning by excluding all internal psychometric items from the feature set, focusing strictly on sixteen socio-environmental and demographic predictors. A quantitative study was conducted with 1413 students in the province of Tungurahua, utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to correct class imbalance. Supervised classification algorithms, including SVM, Random Forest, and XGBoost, were compared. The results demonstrated that the Random Forest model achieved the most balanced performance, reaching an Accuracy of 60.3% and a Macro F1-score of 0.382. Feature importance analysis identified household structure (Living_With_Monoparental) and Family_Coping_Capacity as the most significant predictors of high-risk profiles. These findings provided a statistically honest and ecologically valid tool for Student Counseling Departments (DECE), enabling a transition toward proactive risk identification grounded in observable social vulnerability rather than reactive symptom reporting.
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