Recent studies with laboratory animals indicate that a constellation of behavioral factors predict progression to self-administer drugs. Relatively little is known about behavioral or biological factors that predict the progression in drug use from initial experimentation to regular use in human drug users. The present exploratory study examined reactivity to an acute stressor and reactivity to a single dose of a dopaminergic drug as predictors in progression to heavier smoking in young cigarette smokers over a 6-month period. Forty-four college students who were light to moderate smokers participated in three laboratory sessions, followed by a follow-up interview 6 months later to determine smoking level. On one of the laboratory sessions subjects underwent the Trier Social Stress Test, and on the others they ingested capsules containing placebo or 20 mg D-amphetamine. Outcome measures included subjective ratings of mood and measures of heart rate and salivary cortisol. We found modest positive relationships between stress reactivity and certain responses to amphetamine. Further, stress-induced increases in cortisol were positively related to increases in cigarette smoking in the 31 subjects who we were able to contact at 6 months. Although these results are highly preliminary, they resemble the relationships previously reported in laboratory animals, suggesting that some of the same factors that predict drug-self-administration in rodents predict progression in drug use among young adults.
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http://dx.doi.org/10.1016/j.pbb.2006.07.001 | DOI Listing |
J Law Med
November 2024
Associate Professor, La Trobe Law School, La Trobe University.
Risk assessment is an important component of judicial decision-making in many areas of the law. In Australia, those convicted of terrorist offences may be the subject of continued detention in prison or extended supervision in the community if there is an "unacceptable risk" of them committing future terrorism offences. Forensic psychologists and psychiatrists may provide evidence of risk through identifying and measuring risk factors with the aid of tools that use scales based on statistical or actuarial risk prediction.
View Article and Find Full Text PDFJ Pers Assess
January 2025
Department of Clinical and School Psychology, Nova Southeastern University.
This study evaluated the factorial structure and invariance of the Multidimensional Assessment of Interoceptive Awareness-v2 (MAIA-2). We also investigated incremental validity of the MAIA-2 factors for predicting eating pathology beyond appetite-based interoception. US-based online respondents ( = 1294; =48.
View Article and Find Full Text PDFBMC Psychol
January 2025
Health Department of Kuala Lumpur and Putrajaya, Health office of Lembah Pantai District, Ministry of Health, Kuala Lumpur, Malaysia.
Background: Child maltreatment in daycare is a public health issue. As childcare is stressful, high care provider negativity independently predicts more internalizing behaviour problems, affecting children's psycho-neurological development. This study aimed to determine psychosocial factors associated with the mental health of preschool care providers in Kuala Lumpur.
View Article and Find Full Text PDFSelf-regulated learning (SRL) has been regarded as one of the indispensable factors affecting students' academic success in online learning environments. However, the current understanding of the mechanism/causes of SRL in online ill-structured problem-solving remains insufficient. This study, therefore, examines the configural causal effects of goal attributes, motivational beliefs, creativity, and grit on self-regulated learning.
View Article and Find Full Text PDFBiol Direct
January 2025
School of Medicine, South China University of Technology, Guangzhou, 510006, China.
Background: Pancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies.
Methods: We constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis.
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