David Steyrl

Datos Biográficos

ID182629
NOMBREDavid Steyrl
NOMBRESDavid
APELLIDOSteyrl
FIRMASTEYRL D
AFILIACIONESUniversity of Vienna
ORCID0000-0002-3215-8115
VERIFICADOSí
TOTAL DE OBRAS5
TOTAL DE CITAS0
TOTAL COMO AUTOR5
TOTAL COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2023
AÑO MÁS RECIENTE DE PUBLICACIÓN2025
ÍNDICE H0

  • Study protocol for the Rainbow Austrian Longitudinal Family (Ralf) study

    Open AccessBetty Geidel, Magdalena Siegel et al.ARTICLEBMC Psychology2025

    The RALF study is the first prospective study to comprehensively investigate minority-specific risk and resilience factors in Austrian LGBTQ+ parent families. Findings have the potential to fill key research gaps, inform policy, and guide clinical practices that support LGBTQ+ parent families

  • Minority stress and structural stigma predict well‐being in European LGBTQ + parents

    Open AccessMagdalena Siegel, David Steyrl et al.ARTICLEJournal of Marriage and the Family2025Referencias: 107

    Objective This study tested whether exposure to minority stress and structural stigma across multiple levels of the family system were associated with two indicators of well‐being (life satisfaction, depressive symptoms) in LGBTQ+ parents across 19 European countries. Background Minority stress (i.e., identity‐based stress resulting from systemic oppression) and structural stigma (i.e., hostile legal environments, prejudicial social attitudes) ar…

  • Measuring the semantic priming effect across many languages

    Open AccessErin M Buchanan, Kelly Cuccolo et al.ARTICLENature Human Behaviour2025Referencias: 90

  • The help-seeking process and predictors of mental health care use among individuals with depressive symptoms

    Open AccessVanessa C Jürgensen, Vanessa Juergensen et al.ARTICLEFrontiers in Public Health2024

    Purpose: The goal of the study was to identify the most important influences on professional healthcare use of people with depressive symptoms. We incorporated findings from research areas of health behaviors, stigma, and motivation to predict the help-seeking process variables from a wide range of personal factors and attitudes. Methods: A sample of 1,368 adults with untreated depressive symptoms participated in an online survey with three-and s…

  • Machine learning models predict PTSD severity and functional impairment

    Open AccessAnna H Park, Herry Patel et al.ARTICLEPsychological Trauma Theory…2023

    Our results reinforce that dissociation, cooccurring anxiety and depressive symptoms, maladaptive trauma appraisals, cognitive dysfunction, and emotion dysregulation are critical targets for trauma-related interventions. Machine learning models can inform personalized medicine approaches to maximize trauma recovery in real-world inpatient populations. (PsycInfo Database Record (c) 2025 APA, all rights reserved)