Risk Factors for Mental Health Concerns and Seizures in Pre-teens and Adolescents with Autism Spectrum Disorder (ASD)
Notice bibliographique
Résumé
Objectives: The purpose of this thesis was to identify risk factors for the development of mental health concerns in pre-teens and adolescents with Autism Spectrum Disorder (ASD), and in particular the role of early childhood ASD symptomatology in their development.Additionally, this thesis generated prevalence estimates for mental health concerns in Canadian adolescents with ASD. Methods:The parents of 390 individuals with ASD were invited to participate in a survey, either online or by mail.Sixty-seven parents completed and returned surveys.Kendall tau b correlation coefficients were calculated for the association between age at assessment with ADI-R and score in each domain.Prevalence estimates with 95% confidence intervals were generated, and the Kappa statistic was used to determine the strength of agreement between parent-reported diagnoses and clinical CBCL scores.Finally, bivariate analysis was used to determine if childhood ASD symptomatology was associated with mental health in adolescence, followed by logistic regression modeling to evaluate the effect of other possible risk factors.Results: Scores on two domains of the ADI-R were significantly associated with age at assessment, therefore, it was necessary to control for age at assessment with the ADI-R on these domains in the analysis conducted in Chapter Four.Forty-five percent of the study sample met case criteria for a comorbid psychiatric disorder.Anxiety, mood and attention-deficit disorders were the most common disorders in this sample.Early childhood ASD symptoms were not associated with the development of mental health concerns in adolescence.Family history and female gender were associated with the development of mental health concerns in adolescence. Conclusions:Nearly half of the individuals in our sample have been diagnosed with a psychiatric disorder, or are experiencing clinically significant symptoms that may be indicative of such a disorder.Our findings of discrepancies between parent-reported diagnoses and CBCL scores, indicates that many individuals in our sample are experiencing clinically significant mental health iii concerns, but do not have an official diagnosis.Finally, as has been reported previously, family history of mental illness and female gender were found to be associated with the development of a mental health concern in adolescence.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».