[article]
| Titre : |
Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes |
| Type de document : |
Texte imprimé et/ou numérique |
| Auteurs : |
Aki NIKOLAIDIS, Auteur ; Charlotte HELENIAK, Auteur ; Andrea FIELDS, Auteur ; Paul A. BLOOM, Auteur ; Michelle VANTIEGHEM, Auteur ; Anna VANNUCCI, Auteur ; Nicolas L. CAMACHO, Auteur ; Tricia CHOY, Auteur ; Lisa GIBSON, Auteur ; Chelsea HARMON, Auteur ; Syntia S. HADIS, Auteur ; Ian J. DOUGLAS, Auteur ; Michael P. MILHAM, Auteur ; Nim TOTTENHAM, Auteur |
| Article en page(s) : |
621-634 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
caregiving related early adversities heterogeneity prediction subtyping |
| Index. décimale : |
PER Périodiques |
| Résumé : |
Early psychosocial adversities exist at many levels, including caregiving-related, extrafamilial, and sociodemographic, which despite their high interrelatedness may have unique impacts on development. In this paper, we focus on caregiving-related early adversities (crEAs) and parse the heterogeneity of crEAs via data reduction techniques that identify experiential cooccurrences. Using network science, we characterized crEA cooccurrences to represent the comorbidity of crEA experiences across a sample of school-age children (n = 258; 6?12 years old) with a history of crEAs. crEA dimensions (variable level) and crEA subtypes (subject level) were identified using parallel factor analysis/principal component analysis and graph-based Louvain community detection. Bagging enhancement with cross-validation provided estimates of robustness. These data-driven dimensions/subtypes showed evidence of stability, transcended traditional sociolegally defined groups, were more homogenous than sociolegally defined groups, and reduced statistical correlations with sociodemographic factors. Finally, random forests showed both unique and common predictive importance of the crEA dimensions/subtypes for childhood mental health symptoms and academic skills. These data-driven outcomes provide additional tools and recommendations for crEA data reduction to inform precision medicine efforts in this area. |
| En ligne : |
http://dx.doi.org/10.1017/s0954579421001668 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474 |
in Development and Psychopathology > 34-2 (May 2022) . - 621-634
[article] Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes [Texte imprimé et/ou numérique] / Aki NIKOLAIDIS, Auteur ; Charlotte HELENIAK, Auteur ; Andrea FIELDS, Auteur ; Paul A. BLOOM, Auteur ; Michelle VANTIEGHEM, Auteur ; Anna VANNUCCI, Auteur ; Nicolas L. CAMACHO, Auteur ; Tricia CHOY, Auteur ; Lisa GIBSON, Auteur ; Chelsea HARMON, Auteur ; Syntia S. HADIS, Auteur ; Ian J. DOUGLAS, Auteur ; Michael P. MILHAM, Auteur ; Nim TOTTENHAM, Auteur . - 621-634. Langues : Anglais ( eng) in Development and Psychopathology > 34-2 (May 2022) . - 621-634
| Mots-clés : |
caregiving related early adversities heterogeneity prediction subtyping |
| Index. décimale : |
PER Périodiques |
| Résumé : |
Early psychosocial adversities exist at many levels, including caregiving-related, extrafamilial, and sociodemographic, which despite their high interrelatedness may have unique impacts on development. In this paper, we focus on caregiving-related early adversities (crEAs) and parse the heterogeneity of crEAs via data reduction techniques that identify experiential cooccurrences. Using network science, we characterized crEA cooccurrences to represent the comorbidity of crEA experiences across a sample of school-age children (n = 258; 6?12 years old) with a history of crEAs. crEA dimensions (variable level) and crEA subtypes (subject level) were identified using parallel factor analysis/principal component analysis and graph-based Louvain community detection. Bagging enhancement with cross-validation provided estimates of robustness. These data-driven dimensions/subtypes showed evidence of stability, transcended traditional sociolegally defined groups, were more homogenous than sociolegally defined groups, and reduced statistical correlations with sociodemographic factors. Finally, random forests showed both unique and common predictive importance of the crEA dimensions/subtypes for childhood mental health symptoms and academic skills. These data-driven outcomes provide additional tools and recommendations for crEA data reduction to inform precision medicine efforts in this area. |
| En ligne : |
http://dx.doi.org/10.1017/s0954579421001668 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474 |
|
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