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1.
J Stud Alcohol Drugs ; 2024 Aug 19.
Artículo en Inglés | MEDLINE | ID: mdl-39158017

RESUMEN

OBJECTIVE: The present study aimed to characterize profiles of mental health, incorporating both indicators of psychopathology and well-being, among college students and determine whether institutional belonging differentially relates to past month substance use by mental health profile. METHOD: Students (N = 4018; 59.5% female, 74.7% white) completed a survey regarding mental health (i.e., anxiety symptoms, depressive symptoms, stress, flourishing, academic confidence), institutional belonging, and whether they had engaged in any binge drinking of alcohol and use of cannabis and nicotine products, including nicotine vaping, over the past month. RESULTS: Latent profile analyses indicated five profiles of mental health with differing levels of psychopathology and well-being. Greater institutional belonging was only related to higher odds of binge drinking among students in profiles characterized by average or high well-being, irrespective of psychopathology. Among students with overall poor mental health, higher institutional belonging was related to higher odds of nicotine use. Results were generally invariant to campus and year at college. CONCLUSIONS: Our findings highlight that both positive and negative aspects of mental health should be considered when assessing college students' substance use. Greater institutional belonging may incur risk for substance use differentially by mental health, with respect to binge drinking for those with high levels of positive well-being and non-vaping nicotine use for those with overall poor mental health. Because associations emerged between belonging and substance use risk, institutions could consider implementing or raising awareness of alcohol-free, inclusive activities to ensure that students can feel a sense of belonging while abstaining from drinking.

2.
J Am Coll Health ; : 1-7, 2023 Oct 19.
Artículo en Inglés | MEDLINE | ID: mdl-37856475

RESUMEN

Aim: This study explored the relationship between impostor phenomenon (IP), campus connectedness, self-compassion, and mental health among Black female undergraduate students attending an HBCU. Methods: Participants completed study measures via an online survey platform. Results: Linear regression analyses identified statistically significant relationships between campus connectedness, self-compassion, feelings of IP, and symptoms of depression, anxiety, and stress. Hierarchical regression analyses revealed that both self-compassion and campus connectedness explained statistically significant variance in mental health after controlling for the influence of IP on students' anxiety and depressive symptoms. Conclusion: Implications of this finding suggest student support services could develop and utilize programs that educate students about self-compassion interventions in order to decrease IP, which is directly linked with depression and anxiety.

3.
Front Psychol ; 13: 644243, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35874360

RESUMEN

To explore the influence mechanism and boundary conditions of academic encouragement on college students' academic self-efficacy, this study did a questionnaire survey and used the four scales, namely, Academic Encouragement Scale (AES), Course Subscale of the College Self-Efficacy Inventory (CCSI), Adult Hope Scale (AHS), and Campus Connectedness Scale (CCS). The questionnaires were distributed both online and offline. A total of 355 questionnaires were distributed, with 267 valid returns. Among them, 139 were women (52.1%) and 128 were men (47.9%), and the age range is 18-24 years old. As for the grade level, 123 were first-year college students (46.1%), 58 were second-year college students (21.7%), and 86 were third-year college students (32.2%). The results of this study showed the following. (1) Campus connectedness or hope mediated the relations between (challenge-focused or potential-focused) encouragement and academic self-efficacy. (2) Academic engagement could not moderate the above mediation models.

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