Commentary: Reply to 'Transgender and mental health' by Philip Graham / Alexandra POTTER in Journal of Child Psychology and Psychiatry, 63-2 (February 2022)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 63-2 (February 2022) . - p.246-247 Titre : Commentary: Reply to 'Transgender and mental health' by Philip Graham Type de document : texte imprimé Auteurs : Alexandra POTTER, Auteur Article en page(s) : p.246-247 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : The letter to the editor from Philip Graham regarding the manuscript 'Early adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study' (Potter et al., 2021) raises several points that highlight the complexity of the conversation around gender development in youth. While there is an agreement between the original manuscript and much of the letter, some of the issues raised warrant further discussion and clarification. En ligne : http://dx.doi.org/10.1111/jcpp.13441 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=457 [article] Commentary: Reply to 'Transgender and mental health' by Philip Graham [texte imprimé] / Alexandra POTTER, Auteur . - p.246-247. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 63-2 (February 2022) . - p.246-247 Index. décimale : PER Périodiques Résumé : The letter to the editor from Philip Graham regarding the manuscript 'Early adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study' (Potter et al., 2021) raises several points that highlight the complexity of the conversation around gender development in youth. While there is an agreement between the original manuscript and much of the letter, some of the issues raised warrant further discussion and clarification. En ligne : http://dx.doi.org/10.1111/jcpp.13441 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=457

Early adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study / Alexandra POTTER in Journal of Child Psychology and Psychiatry, 62-2 (February 2021)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 62-2 (February 2021) . - p.171-179 Titre : Early adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study Type de document : texte imprimé Auteurs : Alexandra POTTER, Auteur ; Sarahjane DUBE, Auteur ; Nicholas ALLGAIER, Auteur ; Hannah LOSO, Auteur ; Masha Y. IVANOVA, Auteur ; Lisa C BARRIOS, Auteur ; Susan Y. BOOKHEIMER, Auteur ; Bader CHAARANI, Auteur ; Julie DUMAS, Auteur ; Sarah FELDSTEIN-EWING, Auteur ; Edward G. FREEDMAN, Auteur ; Hugh GARAVAN, Auteur ; Elizabeth HOFFMAN, Auteur ; Erin MCGLADE, Auteur ; Leah ROBIN, Auteur ; Michelle M. JOHNS, Auteur Année de publication : 2021 Article en page(s) : p.171-179 Langues : Anglais (eng) Mots-clés : Gender diversity  early adolescent  gender expression  nonconformity  suicidality  transgender  that they have no competing or potential conflicts of interest. Index. décimale : PER Périodiques Résumé : BACKGROUND: There are known associations between mental health symptoms and transgender identity among adults. Whether this relationship extends to early adolescents and to gender domains other than identity is unclear. This study measured dimensions of gender in a large, diverse, sample of youth, and examined associations between diverse gender experiences and mental health. METHODS: The ABCD study is an ongoing, longitudinal, US cohort study. Baseline data (release 2.0) include 11,873 youth age 9/10 (48% female); and the 4,951 1-year follow-up visits (age 10/11; 48% female) completed prior to data release. A novel gender survey at the 1-year visit assessed felt-gender, gender noncontentedness, and gender nonconformity using a 5-point scale. Mental health measures included youth- and parent-reports. RESULTS: Roughly half a percent of 9/10-year-olds (n = 58) responded 'yes' or 'maybe' when asked, 'Are you transgender' at baseline. Recurrent thoughts of death were more prevalent among these youth compared to the rest of the cohort (19.6% vs. 6.4%, χ(2)  = 16.0, p < .001). At the 1-year visit, when asked about the three dimensions of gender on a 5-point scale, 33.2% (n = 1,605) provided responses that were not exclusively and totally aligned with one gender. Significant relationships were observed between mental health symptoms and gender diversity for all dimensions assessed. CONCLUSIONS: Similar to adult studies, early adolescents identifying as transgender reported increased mental health symptoms. Results also point to considerable diversity in other dimensions of gender (felt-gender, gender noncontentedness, gender nonconformity) among 10/11-year-olds, and find this diversity to be related to critical mental health symptoms. These findings add to our limited understanding of the relationship between dimensions of gender and wellness for youth. En ligne : http://dx.doi.org/10.1111/jcpp.13248 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=440 [article] Early adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study [texte imprimé] / Alexandra POTTER, Auteur ; Sarahjane DUBE, Auteur ; Nicholas ALLGAIER, Auteur ; Hannah LOSO, Auteur ; Masha Y. IVANOVA, Auteur ; Lisa C BARRIOS, Auteur ; Susan Y. BOOKHEIMER, Auteur ; Bader CHAARANI, Auteur ; Julie DUMAS, Auteur ; Sarah FELDSTEIN-EWING, Auteur ; Edward G. FREEDMAN, Auteur ; Hugh GARAVAN, Auteur ; Elizabeth HOFFMAN, Auteur ; Erin MCGLADE, Auteur ; Leah ROBIN, Auteur ; Michelle M. JOHNS, Auteur . - 2021 . - p.171-179. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 62-2 (February 2021) . - p.171-179 Mots-clés : Gender diversity  early adolescent  gender expression  nonconformity  suicidality  transgender  that they have no competing or potential conflicts of interest. Index. décimale : PER Périodiques Résumé : BACKGROUND: There are known associations between mental health symptoms and transgender identity among adults. Whether this relationship extends to early adolescents and to gender domains other than identity is unclear. This study measured dimensions of gender in a large, diverse, sample of youth, and examined associations between diverse gender experiences and mental health. METHODS: The ABCD study is an ongoing, longitudinal, US cohort study. Baseline data (release 2.0) include 11,873 youth age 9/10 (48% female); and the 4,951 1-year follow-up visits (age 10/11; 48% female) completed prior to data release. A novel gender survey at the 1-year visit assessed felt-gender, gender noncontentedness, and gender nonconformity using a 5-point scale. Mental health measures included youth- and parent-reports. RESULTS: Roughly half a percent of 9/10-year-olds (n = 58) responded 'yes' or 'maybe' when asked, 'Are you transgender' at baseline. Recurrent thoughts of death were more prevalent among these youth compared to the rest of the cohort (19.6% vs. 6.4%, χ(2)  = 16.0, p < .001). At the 1-year visit, when asked about the three dimensions of gender on a 5-point scale, 33.2% (n = 1,605) provided responses that were not exclusively and totally aligned with one gender. Significant relationships were observed between mental health symptoms and gender diversity for all dimensions assessed. CONCLUSIONS: Similar to adult studies, early adolescents identifying as transgender reported increased mental health symptoms. Results also point to considerable diversity in other dimensions of gender (felt-gender, gender noncontentedness, gender nonconformity) among 10/11-year-olds, and find this diversity to be related to critical mental health symptoms. These findings add to our limited understanding of the relationship between dimensions of gender and wellness for youth. En ligne : http://dx.doi.org/10.1111/jcpp.13248 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=440

Machine learning prediction of conduct problems in children using the longitudinal ABCD study / Kathryn BERLUTI in Journal of Child Psychology and Psychiatry, 67-3 (March 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.390-399 Titre : Machine learning prediction of conduct problems in children using the longitudinal ABCD study Type de document : texte imprimé Auteurs : Kathryn BERLUTI, Auteur ; Paige AMORMINO, Auteur ; Alexandra POTTER, Auteur ; Safwan WSHAH, Auteur ; Abigail MARSH, Auteur Article en page(s) : p.390-399 Langues : Anglais (eng) Mots-clés : Conduct disorder  conduct problems  machine learning  ABCD study Index. décimale : PER Périodiques Résumé : Background Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at-risk children can aid in providing timely intervention and prevention, ultimately improving their long-term outcomes. There is a need to develop screening tools to better identify at-risk children who may benefit from early intervention. Methods Data were collected from the longitudinal Adolescent Brain Cognitive Development (ABCD) Study. Children completed a baseline visit at age 9?10, then returned annually for 3?years (n?=?3,517). We used machine learning classifiers (logistic regression, Naïve Bayes, support vector machine, and random forest) to predict conduct problems (i.e., conduct disorder or oppositional defiant disorder) in children after 1, 2, and 3?years. Results The best-performing model (the random forest classifier) predicted children at risk for conduct problems with an accuracy of 90% or greater (AUC?=?0.98 at 1?year, AUC?=?0.97 at 2?years, AUC?=?0.97 at 3?years). A random forest classifier simplified to include only 10 features was able to predict conduct problems nearly as well (AUC?=?0.97 at 1?year, AUC?=?0.96 at 2?years, AUC?=?0.97 at 3?years). Conclusions Using factors previously linked to conduct problems, we built machine learning models to identify predictors of conduct problems in children over a 3-year period. A small number of self-report features can be used to predict persistent conduct problems with 90% or greater specificity and sensitivity up to 3?years after initial assessment. This suggests that parent and child self-report data, along with machine learning, can identify children at risk for persistent conduct problems. En ligne : https://doi.org/10.1111/jcpp.70057 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580 [article] Machine learning prediction of conduct problems in children using the longitudinal ABCD study [texte imprimé] / Kathryn BERLUTI, Auteur ; Paige AMORMINO, Auteur ; Alexandra POTTER, Auteur ; Safwan WSHAH, Auteur ; Abigail MARSH, Auteur . - p.390-399. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.390-399 Mots-clés : Conduct disorder  conduct problems  machine learning  ABCD study Index. décimale : PER Périodiques Résumé : Background Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at-risk children can aid in providing timely intervention and prevention, ultimately improving their long-term outcomes. There is a need to develop screening tools to better identify at-risk children who may benefit from early intervention. Methods Data were collected from the longitudinal Adolescent Brain Cognitive Development (ABCD) Study. Children completed a baseline visit at age 9?10, then returned annually for 3?years (n?=?3,517). We used machine learning classifiers (logistic regression, Naïve Bayes, support vector machine, and random forest) to predict conduct problems (i.e., conduct disorder or oppositional defiant disorder) in children after 1, 2, and 3?years. Results The best-performing model (the random forest classifier) predicted children at risk for conduct problems with an accuracy of 90% or greater (AUC?=?0.98 at 1?year, AUC?=?0.97 at 2?years, AUC?=?0.97 at 3?years). A random forest classifier simplified to include only 10 features was able to predict conduct problems nearly as well (AUC?=?0.97 at 1?year, AUC?=?0.96 at 2?years, AUC?=?0.97 at 3?years). Conclusions Using factors previously linked to conduct problems, we built machine learning models to identify predictors of conduct problems in children over a 3-year period. A small number of self-report features can be used to predict persistent conduct problems with 90% or greater specificity and sensitivity up to 3?years after initial assessment. This suggests that parent and child self-report data, along with machine learning, can identify children at risk for persistent conduct problems. En ligne : https://doi.org/10.1111/jcpp.70057 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580

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