Toward an Understanding of Predictive and Protective Factors in Gambling

Monacis, Lucia; Sinatra, María; Tanucci, Giancarlo; Taurino, Alessandro; Palo, Valeria de

doi:10.1016/j.sbspro.2013.12.757

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Our selection of risk factors was informed by recent reviews of the literature [4,8,9,10,20,22,23,24], but was necessarily limited to factors that could be measured by self-report.…”

Section: Methodsmentioning

confidence: 99%

A Multivariate Evaluation of 25 Proximal and Distal Risk-Factors for Gambling-Related Harm

Browne

Hing

Russell

et al. 2019

JCM

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Individual differences in the risk of developing gambling-related harm play an important role in theoretical models and practical interventions. The present study attempted comprehensive measurement and evaluation of 25 known risk factors for gambling-related harm in order to determine which factors provided large and unique explanatory power. We surveyed 1650 regular gamblers from an online panel, screening in 1174 (466 male) who passed all checks of attention and response consistency. We evaluated each risk factor based on bivariate correlations with harms, then made separate multivariate evaluations of proximal (e.g., gambling motivations) and distal (e.g., religiosity) risk factors. Almost all bivariate correlations were significant, but most distal factors were not significant in multivariate models. Trait impulsivity was the most important risk factor by a large margin. Excessive consumption, less use of safe gambling practices, and more fallacies were key proximal risks of harm. Many well-known correlates of gambling harm (e.g., youth, lower educational attainment) do not show a direct role in the development of gambling harm when controlling for other factors. The results support theoretical models that emphasise early conditioning and biological vulnerability (manifested through impulsivity). Since maladaptive cognitive and behavioural schemas appear to be more important than motivations (e.g., escape, excitement, ego), interventions may benefit by targeting these proximal drivers of harm.

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“…Our selection of risk factors was informed by recent reviews of the literature [4,8,9,10,20,22,23,24], but was necessarily limited to factors that could be measured by self-report.…”

Section: Methodsmentioning

confidence: 99%

A Multivariate Evaluation of 25 Proximal and Distal Risk-Factors for Gambling-Related Harm

Browne

Hing

Russell

et al. 2019

JCM

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“…Además, de la posibilidad de crear vínculos de trabajo integrado, desarrollar acciones de colaboración y cooperación para el trabajo en equipo, influenciar en el estudiante a vincularse mejor con la universidad para "despertar" sus potencialidades que muchas veces (por la experiencia docente) no son convenientemente exploradas por el sistema universitario (Alvaréz Botello et al, 2015;Martin y Marsh, 2009;Rovira-Collado, 2016). Modelar dominios orientados a desarrollar perfiles de estudiantes para mejorar y asegurar la calidad de la vida académica universitaria es pertinente es una necesidad en las actuales circunstancias, productos de los profundos cambios en el contexto universitario (Monacis et al, 2014).…”

Section: Discusión Y Conclusionesunclassified

Factores de Resiliencia y Afrontamiento como Predictores del Rendimiento Académico de los Estudiantes en Universidades Privadas

Tipismana¹

2019

REICE

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Predecir el rendimiento académico en estudiantes universitarios peruanos es una garantía para cumplir las exigencias de licenciamiento y acreditación. Se realiza un diseño transversal, comparativo y explicativo para establecer los factores de resiliencia y afrontamiento que predicen el rendimiento académico, en dos muestras probabilísticas estratificadas de universidades privadas de Lima. El RESI-, CAE, Smilkstein, CP-LS y estimadores DWLS, GLS, demostraron que el modelo predictivo de resiliencia y promedio ponderado tienen efecto en la percepción de rendimiento académico y un efecto mínimo en el promedio ponderado. Ambos factores predicen de manera directa, indirecta y relacional el rendimiento académico. Destacan los factores confianza en sí mismo, capacidad social, estrategias de autofocalización emocional abierta, exposición emocional abierta y apoyo familiar social como predictores relevantes en expectativa de logro, aprendizaje percibido y satisfacción global. El funcionamiento familiar, es un predictor indirecto a través de la resiliencia del rendimiento académico. Factores de afrontamiento activo, predicen mejor la percepción del rendimiento que factores de protección y riesgo. Muestras homogéneas grandes con valor psicométrico pueden mejorar los modelos predictivos, identificar perfiles, diseñar estudios causales, desarrollar programas de aprendizaje de talento y estrategias activas que mejorarían la calidad del aprendizaje universitario.Descriptores: Rendimiento escolar; Estudiante universitario; Perú; Calidad de la educación; Enseñanza privada.Predicting academic performance in Peruvian university students is a guarantee to meet licensing and accreditation requirements. A transversal, comparative and explanatory (predictive) design was used to establish the resilience and coping factors that predict academic performance, in two stratified probabilistic samples from private universities in Lima. The RESI-, CAE, Smilkstein, CP-LS and DWLS estimators, GLS, showed that the predictive model of resilience and weighted average have an effect on the perception of academic performance and a minimal effect on the weighted average. Both factors predict academic performance directly, indirectly and relationally. The self-confidence factor, social capacity, open emotional self-targeting strategies, open emotional exposure and social family support are highlighted as relevant predictors in expectation of achievement, perceived learning and overall satisfaction. Family functioning is an indirect predictor through the resilience of academic performance. Factors of active coping better predict the perception of performance than protection and risk factors. Large homogeneous samples with psychometric value can improve predictive models, identify profiles, design causal studies, develop talent learning programs and active strategies that would improve the quality of university learning.

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Examining the Latent Structure of Gambling Motivation and Its Relationship with Gambling Behavior in Gambling-Exposed Chinese Casino Employees

Mordeno

Coteriano²,

Leong

2017

Psychol Stud

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Toward an Understanding of Predictive and Protective Factors in Gambling

Cited by 3 publications

References 40 publications

A Multivariate Evaluation of 25 Proximal and Distal Risk-Factors for Gambling-Related Harm

A Multivariate Evaluation of 25 Proximal and Distal Risk-Factors for Gambling-Related Harm

Factores de Resiliencia y Afrontamiento como Predictores del Rendimiento Académico de los Estudiantes en Universidades Privadas

Examining the Latent Structure of Gambling Motivation and Its Relationship with Gambling Behavior in Gambling-Exposed Chinese Casino Employees

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