MétaCan
Menu
Retour à la cohorte
Enregistrement W2345157831 · doi:10.1111/jir.12294

The intersection of autism spectrum disorder and intellectual disability

2016· editorial· en· W2345157831 sur OpenAlexaboutno aff
Connie Kasari

Notice bibliographique

RevueJournal of Intellectual Disability Research · 2016
Typeeditorial
Langueen
DomaineNeuroscience
ThématiqueAutism Spectrum Disorder Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAutismAutism spectrum disorderIntellectual disabilityPsychologyMental healthIntersection (aeronautics)Developmental psychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Old ideas can sometimes use new buildings. New ideas must use old buildings. * Such is the gist of this Special Issue of JIDR on autism spectrum disorder (ASD), the first ever for the journal. In seeking papers that addressed the intersection of both ASD and intellectual disability (ID), we were struck by the importance of drawing together the best of both fields. We found that autism-specific instruments and traditional thinking were not always the most accurate for researching populations with ID. Similarly, traditional thinking about developmental constructs in ID, such as in the area of language, could not adequately describe the unique and often uneven profiles in autism. It is interesting that ID is a very common co-morbid disorder with ASD, yet the field over the last decade or two has paid little attention to this fact (Lecavalier, Snow & Norris, 2011), although the definition of ASD in DSM 5 (APA, 2013) may catalyse research in this area. We do know that in a sample of 13-year-old adolescents with ASD whose IQs fell across the spectrum, IQ did not serve as a protective factor for clinically significant behaviour problems or mental health disorders. Yet, same-aged individuals with ID had three times the risk of developing mental health disorders over typically developing adolescents (Baker & Blacher, 2015). While co-morbidity of ASD and ID does not characterise all of the papers in this Special Issue, as we had hoped, most of the papers do present either new ideas or utilise new approaches to the study of ASD. Thus, the papers accepted herein reflect three broad areas: 1) assessment, where the architecture of developmental theory holds up; 2) language, where researchers grapple with using traditional language approaches to study children with ASD and ID; and 3) social context, which in many ways is the ultimate landscape for studying children with this co-morbid disorder. We provide an overview of this Special Issue as follows. Broadening the assessment of ASD. With the now accepted increase in the prevalence of ASD, researchers and professionals are in need of better and briefer instruments for autism screening and assessment. The Bayley Scales of Infant Development have long been useful for cognitive and language assessment of young typically developing children before age 3; the instrument is well-known, psychometrically sound and requires training for its administration. In Assessment of cognition and language in the early diagnosis of autism spectrum disorder: Usefulness of the Bayley Scales of Infant and Toddler Development (3rd Ed.), Montserrat Torras-Mañá and colleagues evaluated the predictive validity of an earlier administered Bayley at age 4 for Spanish children who had a confirmed ASD diagnosis. At the other end of the developmental spectrum, the article by Charlotte Mutsaerts and colleagues, Screening for ASD in adults with ID: Moving toward a standard using the DiBAS-R and the ACL, indicated improved sensitivity and specificity using two screening instruments for more accurate assessment of autism in adults co-morbid with ID. Language processes and outcomes. Four papers in this Special Issue focus more specifically on aspects of language, an area of concern especially in early childhood for children with both ASD and ID. Assessing and understanding the language profiles of children with ASD – with or without ID – have long challenged researchers, and Alexandra Key and colleagues addressed these issues in Consonant differentiation mediates the discrepancy between nonverbal and verbal abilities in children with ASD. Two papers examined effects of early intervention on language outcomes. Lauren Hampton and Ann Kaiser reviewed literature in Early intervention effects on spoken-language outcomes for children with autism: A systematic review and meta-analysis, and suggest that more thorough reporting of critical variables affecting language intervention studies implemented by parents and/or therapists in ASD would better move the field forward. Veronica Rose and colleagues examined the language outcomes of a large cohort of children with ASD in community intervention, comparing definitions for ‘minimally verbal’ in The proportion of minimally verbal children with autism spectrum disorder in a community-based early intervention program. Finally, a more nuanced approach to the study of imitation in children with ASD is provided by Mikael Heimann and colleagues in Children with autism respond differently to spontaneous, elicited, and deferred imitation. Family and social context. In recognition of the growing literature pertaining to families of children and adults with ASD, the paper by Meghan Burke and Tamar Heller, Individual, parent, and social-environmental correlates of caregiving experiences among parents of adults with autism spectrum disorder, offers links between findings about the impact of adults with ASD and public policy. Importantly, the findings indicate that future planning for one's adult offspring is related to one's current caregiving satisfaction, which appears to be more influenced by the target adult's health and maladaptive behaviours than the ID. Liezl Schlebusch and colleagues provide a rich description of family routines in households with a young child with ASD in their paper, South African families raising children with autism spectrum disorder: Relationship between family routines, cognitive appraisal, and family quality of life. They found that the relationship between the family routines and family quality of life was partially accounted for by parent appraisal of the impact of the child with ASD. Two papers addressed issues of service delivery. Sandra Magaña and colleagues, in Functional severity and Latino ethnicity in specialty services for children with autism spectrum disorder, explored the issue of equity in service delivery. Using multivariate linear and logistic regression models, the authors determined from secondary analyses of a large national dataset that Latino children with severe limitations (ID) received fewer targeted autism services than white non-Latino children with similarly severe limitations. Azin Taheri and colleagues, in Examining the social participation of children with intellectual disabilities and autism spectrum disorder in relations to peers, considered the social context of peers and community activities in a large Canadian sample of children and youth who were typically developing (TD), or who had ID or ASD + ID. Although the youth with ID or ASD + ID had fewer opportunities for socialisation and fewer friends than the TD group, those with ASD + ID had significantly fewer school friends than did the ID group, suggesting that these children may be in the greatest need for school-based social skills intervention. All in all, this first ever issue of JIDR devoted to ASD and ID is but a beginning. We hope that it inspires more work in this area and more cross-fertilisation of researchers interested in both ID and ASD.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,233
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,341
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0130,233
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,018
Communication savante0,0010,001
Science ouverte0,0030,002
Intégrité de la recherche0,0010,008
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,049
Tête enseignante GPT0,382
Écart entre enseignants0,332 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Intellectual Disability ResearchMême sujetAutism Spectrum Disorder ResearchTravaux en français237 207