Objective: The current investigation examined the relation between credibility ratings for adult psychotherapies and a variety of patient factors as well as the relation between credibility ratings and subsequent symptom change.
Method: A pooled study database that included studies evaluating the efficacy of cognitive and psychodynamic therapies for a variety of disorders was used. For all studies, a three-item credibility scale was administered at session 2. Patient variables at baseline were used to predict early treatment credibility.
Results: Early symptom improvement, age, education, and expectation of improvement were all significantly predictive of credibility scores at session 2. In one combined multiple regression model controlling for treatment, study, and early symptom change, age, education, and expectation of improvement remained significantly predictive of credibility scores. Credibility was predictive of subsequent symptom change even when controlling for age, education, expectation of improvement, and early symptom improvement.
Conclusions: These findings suggest that age and education, in addition to expectations of improvement and the amount of early symptom improvement, may influence the patient's perceptions of the credibility of a treatment rationale early in the treatment process and that credibility ratings predict subsequent symptom change.
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http://dx.doi.org/10.1080/10503307.2013.847988 | DOI Listing |
J Clin Exp Neuropsychol
January 2025
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Introduction: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process required to detect these noncredible symptoms. The present study investigated whether unsupervised machine learning (ML) could serve as one such method, and detect noncredible symptom reporting in adults undergoing ADHD evaluations.
View Article and Find Full Text PDFAm J Health Promot
January 2025
College of Social Work, University of South Carolina, Columbia, SC, USA.
Purpose: Artificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Therefore, our purpose was to evaluate and compare the accuracy, completeness, readability, and credibility of responses from standard and advanced versions of popular chatbots: ChatGPT-3.5, ChatGPT-4.
View Article and Find Full Text PDFInt J Drug Policy
January 2025
Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA. Electronic address:
Background: Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical.
Methods: Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020).
Objective: This systematic review and network meta-analysis aimed to compare and evaluate the efficacy and safety of five medications, dupilumab, tralokinumab, upadacitinib, baricitinib, and abrocitinib, for the treatment of adolescent atopic dermatitis, in order to provide decision support to support clinical decision-making by developing more scientifically-grounded and effective treatment strategies.
Methods: A comprehensive search was conducted in PubMed, Embase, Web of Science (WoS), and the Cochrane database to collect randomized controlled trials (RCTs) and Phase 3 clinical trials. Supplementary data were retrieved from trial registries, and researchers contacted study authors and pharmaceutical companies when necessary to obtain complete data.
J Med Internet Res
January 2025
Behavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.
Background: If the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.
Objective: This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.
Methods: Adult smokers from the United Kingdom were invited to participate in a discrete choice experiment.
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