RESUMEN
BACKGROUND: The present study aimed to distinguish cannabis consumption patterns among adolescents and to relate these to life satisfaction, academic achievement, and the use of other psychoactive substances. METHODS: This study used a prospective design. Cannabis use was measured 14 times over the course of 10 years. Participants were 318 adolescents aged 14 to 15 at the beginning of the study. RESULTS: Growth mixture modeling identified 2 latent classes. Class 1 was defined by phases of high-frequency cannabis use, and Class 2 was defined by low-frequency use or nonuse. Class 1 reported decreased satisfaction with life and one's own academic and professional achievement at the age of 24 as well as higher use of tobacco and illicit substances. CONCLUSIONS: High-frequency use of cannabis predicts a decreased satisfaction with life and one's own academic and professional achievement as well as an increased use of other substances.
Asunto(s)
Logro , Abuso de Marihuana/epidemiología , Fumar Marihuana/epidemiología , Modelos Estadísticos , Satisfacción Personal , Uso de Tabaco/epidemiología , Adolescente , Estudios de Cohortes , Progresión de la Enfermedad , Femenino , Alemania/epidemiología , Humanos , Estudios Longitudinales , Masculino , Estudios Prospectivos , Trastornos Relacionados con Sustancias/epidemiología , Adulto JovenRESUMEN
BACKGROUND: The paper analyses how resilience factors and mental health problems interrelate in a 3-year-longitudinal study with 16-19 year olds. METHODS: Resilience was measured with a 13-item short version of the Life-Orientation-Scale by Antonovsky (sense-of-coherence, SOC) and a 10-item self-efficacy-scale (SWE) by Jerusalem and Schwarzer. Mental health problems were measured with Derogatis Symptom Check list (SCL-90-R). The data set included 155 participants and was analyzed using Structural Equation Modeling (SEM) designed to examine mutual influence in longitudinal data with Mplus software. RESULTS: The descriptive data analysis indicates (1) negative correlations between SOC and SCL-90-R at both age 16 and 19 in all subscales but somatization and likewise (2) between self-efficacy and SCL-90-R. (3) SOC correlates positively with SWE at age 16 and 19. Results of SEM analysis were based on the assumption of two latent variables at two points in time: resilience as measured with mean SOC and mean self-efficacy scores and health problems measured with sub scale scores of SCL-90-R - both at ages 16 and 19. The first SEM model included all possible paths between the two latent variables across time. We found (4) that resilience influences mental health problems cross-sectionally at age 16 and at age 19 but not across time. (5) Both resilience and mental health problems influenced their own development over time. A respecified SEM model included only significant paths. (6) Resilience at age 16 significantly influences health problems at age 16 as well as resilience at age 19. Health problems at age 16 influence those at age 19 and resilience at age 19 influences health problems at age 19. CONCLUSION: (a) SOC and self-efficacy instruments measure similar phenomena. (b) Since an influence of resilience on mental health problems and vice versa over time could not be shown there must be additional factors important to development. (c) SOC and self-efficacy are both very stable at 16 and 19 years. This refutes Antonovsky's assumption that SOC achieves stability first around the age of 30. SOC and self-efficacy are protective factors but they seem to form in (early) childhood.