An evaluation of wayfinding abilities in adolescent and young adult males with autism spectrum disorder / Yingying YANG in Research in Autism Spectrum Disorders, 80 (February 2021)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 80 (February 2021) . - p.101697 Titre : An evaluation of wayfinding abilities in adolescent and young adult males with autism spectrum disorder Type de document : texte imprimé Auteurs : Yingying YANG, Auteur ; Weijia LI, Auteur ; Dan HUANG, Auteur ; Wei HE, Auteur ; Yanxi ZHANG, Auteur ; Edward MERRILL, Auteur Article en page(s) : p.101697 Langues : Anglais (eng) Mots-clés : Route learning  Survey learning  Perspective taking  Autism  Parents  Wayfinding Index. décimale : PER Périodiques Résumé : Background Wayfinding refers to traveling from place to place in the environment. Despite some research headway, it remains unclear whether individuals with Autism Spectrum Disorder (ASD) show strengths, weaknesses, or similarities in wayfinding compared with ability-matched typically developing (TD) controls. Method The current study tested 24 individuals with ASD, 24 mental-ability (MA) matched TD (MA-TD) controls, and 24 chronological-age (CA) matched TD (CA-TD) controls. Participants completed a route learning task and a survey learning task, both programmed in virtual environments, and a perspective taking task. Their parents completed questionnaires assessing their children’s everyday wayfinding activities and competence. Results Overall, CA-TD controls performed better than both the ASD group and the MA-TD group in both wayfinding tasks and the perspective taking task. Individuals with ASD performed similarly to the MA- TD controls on wayfinding performance except for backtracking routes. Perspective taking presented an area of deficit for people with ASD and it predicted individual differences in route learning and survey learning. Parents’ reports did not predict their children’s wayfinding performance. Two mini meta-analyses, including previous studies and the current study, showed a significant deficit in route learning, but not in survey learning for the ASD group relative to MA-TD controls. Conclusions Although participants with ASD showed impairments in wayfinding relative to CA-TD controls, the impairment is not specific to their ASD, but rather due to their mental age. Nevertheless, route reversal in route learning may present unique difficulty for people with ASD beyond the effects of mental age. En ligne : https://doi.org/10.1016/j.rasd.2020.101697 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=438 [article] An evaluation of wayfinding abilities in adolescent and young adult males with autism spectrum disorder [texte imprimé] / Yingying YANG, Auteur ; Weijia LI, Auteur ; Dan HUANG, Auteur ; Wei HE, Auteur ; Yanxi ZHANG, Auteur ; Edward MERRILL, Auteur . - p.101697. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 80 (February 2021) . - p.101697 Mots-clés : Route learning  Survey learning  Perspective taking  Autism  Parents  Wayfinding Index. décimale : PER Périodiques Résumé : Background Wayfinding refers to traveling from place to place in the environment. Despite some research headway, it remains unclear whether individuals with Autism Spectrum Disorder (ASD) show strengths, weaknesses, or similarities in wayfinding compared with ability-matched typically developing (TD) controls. Method The current study tested 24 individuals with ASD, 24 mental-ability (MA) matched TD (MA-TD) controls, and 24 chronological-age (CA) matched TD (CA-TD) controls. Participants completed a route learning task and a survey learning task, both programmed in virtual environments, and a perspective taking task. Their parents completed questionnaires assessing their children’s everyday wayfinding activities and competence. Results Overall, CA-TD controls performed better than both the ASD group and the MA-TD group in both wayfinding tasks and the perspective taking task. Individuals with ASD performed similarly to the MA- TD controls on wayfinding performance except for backtracking routes. Perspective taking presented an area of deficit for people with ASD and it predicted individual differences in route learning and survey learning. Parents’ reports did not predict their children’s wayfinding performance. Two mini meta-analyses, including previous studies and the current study, showed a significant deficit in route learning, but not in survey learning for the ASD group relative to MA-TD controls. Conclusions Although participants with ASD showed impairments in wayfinding relative to CA-TD controls, the impairment is not specific to their ASD, but rather due to their mental age. Nevertheless, route reversal in route learning may present unique difficulty for people with ASD beyond the effects of mental age. En ligne : https://doi.org/10.1016/j.rasd.2020.101697 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=438

Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk / Weijia LI in Journal of Child Psychology and Psychiatry, 67-9 (September 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-9 (September 2026) . - p.1576-1589 Titre : Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk Type de document : texte imprimé Auteurs : Weijia LI, Auteur ; Yongtian CHENG, Auteur ; Zeming ZHANG, Auteur ; Yingying YANG, Auteur ; Yanpeng JIN, Auteur ; Zhenhua CUI, Auteur ; Juan WANG, Auteur ; Yinan DUAN, Auteur ; Runsen CHEN, Auteur Article en page(s) : p.1576-1589 Langues : Anglais (eng) Mots-clés : Adolescent  suicide risk screening  machine learning  school  longitudinal validation Index. décimale : PER Périodiques Résumé : Background Adolescent suicide remains a significant public health concern, yet existing suicide screening instruments primarily focus on already manifested suicidal phenomena, underscoring the need for reliable and practical tools to enable early identification and intervention. Methods Based on a large-scale school-based cohort study conducted in Southern China in 2022, this study aimed to develop and preliminarily validate two machine learning-based tools (a 51-item full version and an 11-item abbreviated version) designed to help identify adolescents at risk of developing suicide risk. The dataset was divided into two samples for tool development and longitudinal interview validation. During the tool development phase, LASSO regression was employed to select items with optimal contributions for recent suicide attempts from a multidimensional set of risk factors, followed by model training with multiple machine learning algorithms. The developed models were subsequently evaluated for their ability to predict suicide risk as assessed by the follow-up interview in the longitudinal validation phase. Results Both versions of the screening tool demonstrated adequate discriminative ability, with the CatBoost algorithm outperforming others (AUROC?≥?0.87). The abbreviated tool showed a slight trade-off between model precision and practicality, with a 0.02 reduction in AUROC, while still maintaining appropriate discrimination. Longitudinal validation using follow-up interview outcomes supported the predictive validity of both tools. These findings provide preliminary evidence for the utility of machine learning-based suicide risk screening tools among adolescents. Conclusions This study provides evidence supporting the machine learning-based screening tools for early suicide risk detection in adolescents that integrates multidimensional vulnerabilities. The tools show promise in facilitating early identification and targeted interventions in school settings, addressing a critical need in adolescent mental health care. Nonetheless, further research is warranted to confirm their efficacy and support broader implementation. En ligne : https://doi.org/10.1111/jcpp.70160 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592 [article] Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk [texte imprimé] / Weijia LI, Auteur ; Yongtian CHENG, Auteur ; Zeming ZHANG, Auteur ; Yingying YANG, Auteur ; Yanpeng JIN, Auteur ; Zhenhua CUI, Auteur ; Juan WANG, Auteur ; Yinan DUAN, Auteur ; Runsen CHEN, Auteur . - p.1576-1589. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-9 (September 2026) . - p.1576-1589 Mots-clés : Adolescent  suicide risk screening  machine learning  school  longitudinal validation Index. décimale : PER Périodiques Résumé : Background Adolescent suicide remains a significant public health concern, yet existing suicide screening instruments primarily focus on already manifested suicidal phenomena, underscoring the need for reliable and practical tools to enable early identification and intervention. Methods Based on a large-scale school-based cohort study conducted in Southern China in 2022, this study aimed to develop and preliminarily validate two machine learning-based tools (a 51-item full version and an 11-item abbreviated version) designed to help identify adolescents at risk of developing suicide risk. The dataset was divided into two samples for tool development and longitudinal interview validation. During the tool development phase, LASSO regression was employed to select items with optimal contributions for recent suicide attempts from a multidimensional set of risk factors, followed by model training with multiple machine learning algorithms. The developed models were subsequently evaluated for their ability to predict suicide risk as assessed by the follow-up interview in the longitudinal validation phase. Results Both versions of the screening tool demonstrated adequate discriminative ability, with the CatBoost algorithm outperforming others (AUROC?≥?0.87). The abbreviated tool showed a slight trade-off between model precision and practicality, with a 0.02 reduction in AUROC, while still maintaining appropriate discrimination. Longitudinal validation using follow-up interview outcomes supported the predictive validity of both tools. These findings provide preliminary evidence for the utility of machine learning-based suicide risk screening tools among adolescents. Conclusions This study provides evidence supporting the machine learning-based screening tools for early suicide risk detection in adolescents that integrates multidimensional vulnerabilities. The tools show promise in facilitating early identification and targeted interventions in school settings, addressing a critical need in adolescent mental health care. Nonetheless, further research is warranted to confirm their efficacy and support broader implementation. En ligne : https://doi.org/10.1111/jcpp.70160 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592

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