This study assessed the effectiveness of a web-based cognitive behavioral intervention (CBT) in reducing perfectionism and psychological distress in post-secondary students. Participants assessed as high in perfectionism (n=77) were randomized to one of three 10-week, web-based, intervention conditions (no treatment [NT], general stress management [GSM], or CBT). Results indicated the CBT condition was effective in reducing perfectionism, and supported a pattern of significantly greater improvement than observed in participants in the GSM or NT conditions. While both CBT and GSM demonstrated capacities to significantly reduce distress, for CBT participants changes in perfectionism were significantly correlated with changes in depression and anxiety. Results offer support for the effectiveness of web-based CBT in positively affecting perfectionist-related problems. Given the considerable proportion of individuals who suffer from perfectionism-related distress, the intervention's apparent effectiveness, cost-effectiveness and ease of dissemination warrant future replication studies.
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http://dx.doi.org/10.1080/10503307.2011.637242 | DOI Listing |
Transl Behav Med
January 2025
Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center, Tampa, FL, 33162, USA.
Background: Results of the National Lung Screening Trial create the potential to reduce lung cancer mortality, but community translation of lung cancer screening (LCS) has been challenging. Subsequent policies have endorsed informed and shared decision-making and using decision support tools to support person-centered choices about screening to facilitate implementation. This study evaluated the feasibility and acceptability of LuCaS CHOICES, a web-based decision aid to support delivery of accurate information, facilitate communication skill development, and clarify personal preferences regarding LCS-a key component of high-quality LCS implementation.
View Article and Find Full Text PDFLiver Int
February 2025
Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.
Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.
ACS Pharmacol Transl Sci
January 2025
College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea.
Everolimus presents significant dosing challenges due to between- and within-patient pharmacokinetic variabilities. This study aimed to develop and validate a model-informed precision dosing strategy for everolimus in liver transplant recipients. The dosing strategy was initially developed using retrospective data, employing nonlinear mixed-effects modeling.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
March 2024
Military Population Health Directorate, Naval Health Research Center, San Diego, CA, United States.
Background: Adolescence is a particularly sensitive period of development for military-connected youth, given the socioemotional and physical changes that occur against the backdrop of the military career of their parent(s). Military-connected adolescents face unique stressors relative to their civilian counterparts, such as military relocations, parental absence due to deployments and trainings, and parental military-related physical and mental injury. These stressors may change family dynamics and disrupt social support networks, which can have lasting implications for adolescent health and well-being.
View Article and Find Full Text PDFSci Rep
January 2025
Pharmacy Department, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE1 4LP, England, UK.
Prescribing errors are a source of preventable harm in healthcare, which may be mitigated using Electronic Prescribing (EP) systems. Anyone who routinely prescribes medication could benefit from digitally assisted automated checks to identify whether a prescription should potentially not be allowed (e.g.
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