12 Diagnosing autism in children with down syndrome: Caregivers' perspectives
Notice bibliographique
Résumé
Abstract Background Autism Spectrum Disorder (ASD) is more common in individuals with Down Syndrome (DS) than in the general population, with prevalence rates as high as 41%. Diagnosing ASD in children with DS is challenging, often resulting in delays and missed opportunities for early intervention. While research has primarily focused on the clinical complexities of diagnosing ASD in this population, far less attention has been given to the experiences of caregivers, whose perspectives offer valuable insights into the diagnostic process. Objectives The purpose of this study is to explore the diagnostic journeys of caregivers of children with DS diagnosed with ASD. The goal is to identify the challenges and needs of these families, the barriers they encounter within the healthcare system, and how ASD symptomology presents in their children. Design/Methods This study used a cross-sectional mixed-methods design. Quantitative data were collected through anonymous electronic structured survey (n=21), and qualitative data from semi-structured interviews (n=11) conducted over Zoom, audio-recorded, and transcribed verbatim. Participants were caregivers of children under 18 with confirmed DS-ASD diagnoses, recruited through Down syndrome associations across Canada. Survey responses were analyzed using descriptive statistics, and interview data underwent inductive thematic analysis. Data were collected from March to June 2024, after ethical approval. Results Participants were primarily from Ontario (86%), with smaller groups from British Columbia and Alberta. Caregivers described confusion throughout the diagnostic process, feeling "lost in the system" and "repeatedly dismissed" by healthcare professionals. Most said their concerns were "not taken seriously," with behaviors frequently attributed as “normal” or “typical” for a child with DS. For most, ASD diagnosis brought emotional relief, helping them understand their child’s behaviors, but faced challenges accessing tailored interventions for DS-ASD. Quantitative data showed the mean age of ASD symptom onset was 2.76 years (SD = 1.73). Diagnoses occurred between 3 and 12 years, with a median age of 6. Most diagnoses were delayed by about four years; however, some children were diagnosed by age 4, these were children with a family history of ASD or intellectual disability, early regression, and had access to specialized centers. Common early ASD symptoms included repetitive behaviors (100%), speech delays/regression (90.5%), and sensory sensitivities (62%). Conclusion The findings highlight the need for greater awareness, specialized training, and tailored interventions addressing both DS and ASD. Caregivers' concerns should be validated and incorporated into decisions about early ASD assessments. The four-year average delay in diagnosis, along with symptom profiles, aligns with published literature.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».