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Background And Objectives: Previous studies have found high implicit self-esteem (ISE) to prevail concurrently with low explicit self-esteem (ESE) in socially anxious adults. This suggests that self-esteem discrepancies are associated with social anxiety disorder (SAD). Given that the onset of SAD often occurs in adolescence, we investigated self-esteem discrepancies between ISE and ESE in adolescents suffering from SAD.
Methods: Two implicit measures (Affect Misattribution Procedure, Implicit Association Test) were used both before and after a social threat activation in 20 adolescents with SAD (14-20 years), and compared to 20 healthy adolescents who were matched for age and gender. The Rosenberg Self-Esteem Scale, the Social Cognitions Questionnaire and Beck Depression Inventory were administered as explicit measures. We expected discrepant self-esteem (high ISE, low ESE) in adolescents with SAD, in comparison to congruent self-esteem (positive ISE, positive ESE) in healthy controls, after social threat activation.
Results: Both the patient and control groups exhibited high positive ISE on both implicit measures, before as well as after social threat induction. Explicitly, patients suffering from SAD revealed lower levels of ESE, compared to the healthy adolescents.
Conclusions: This study is the first to examine ISE and ESE in a clinical sample of adolescent patients with SAD. Our results suggest that SAD is associated with a discrepancy between high ISE and low ESE, after a social-threat manipulation. The findings are discussed in relation to other studies using implicit measures in SAD and may provide a more comprehensive understanding of the role of self-esteem in adolescent SAD.
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http://dx.doi.org/10.1016/j.jbtep.2012.05.003 | DOI Listing |
J Med Internet Res
September 2025
Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, United States, 1 2122416500.
Background: The growing adoption of diagnostic and prognostic algorithms in health care has led to concerns about the perpetuation of algorithmic bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success and tradeoffs. However, there have been limited substantive efforts to address bias at the level of the data used to generate algorithms in health care datasets.
View Article and Find Full Text PDFBorderline Personal Disord Emot Dysregul
September 2025
Independent Researcher, Berlin, Germany.
Background: The tendency to forgive is associated with traits such as agreeableness and neuroticism, mental well-being, and interpersonal functioning. Given documented associations with interpersonal conflict and aggression in borderline personality disorder (BPD), forgiveness (or, lack thereof) may be particularly relevant for BPD symptomatology but remains understudied. This study examines forgiveness in BPD compared to a heterogeneous clinical control group without personality disorder (CC), exploring its associations with aggression and interpersonal dysfunction using both direct (self-reported) and indirect (implicit) measures.
View Article and Find Full Text PDFJ Med Ethics
September 2025
Uehiro Oxford Institute, Oxford University, Oxford, UK
Warnings that large language models (LLMs) could 'dehumanise' medical decision-making often rest on an asymmetrical comparison: the idealised, attentive healthcare provider versus a clumsy, early-stage artificial intelligence (AI). This framing ignores a more urgent reality: many patients face rushed, jargon-heavy, inconsistent communication, even from skilled professionals. This response to Hildebrand's critique argues that: (1) while he worries patients lose a safeguard against family pressure, in practice, time pressure, uncertainty and fragile dynamics often prevent clinician intervention.
View Article and Find Full Text PDFJ Pharm Policy Pract
August 2025
Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Penang, Malaysia.
Over the past few decades, the emergence of irrational medicine use has become a significant global health challenge. It has contributed to medication errors, adverse drug reactions, higher treatment costs, increased morbidity, and mortality. Problems with irrational prescribing are a matter of concern in low and middle-income countries, where regulatory control is underdeveloped, healthcare systems are constrained by economic pressures, and there is a shortage of trained personnel.
View Article and Find Full Text PDFJ Cheminform
September 2025
Institute for computer science, Osnabrück University, Friedrich-Janssen-Str. 1, 49076, Osnabrück, Lower Saxony, Germany.
Deriving symbolic knowledge from trained deep learning models is challenging due to the lack of transparency in such models. A promising approach to address this issue is to couple a semantic structure with the model outputs and thereby make the model interpretable. In prediction tasks such as multi-label classification, labels tend to form hierarchical relationships.
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