12 Diagnosing autism in children with down syndrome: Caregivers' perspectives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".