A cross-sectional examination of service complexity in youths with co-occurring autism spectrum disorder and psychiatric or medical diagnoses across service sectors
Bibliographic record
Abstract
Introduction: Autism spectrum disorder (ASD) is a heterogeneous, life-long, and complex condition. Youth diagnosed with ASD require several supports addressing core symptoms associated with the disorder, but also those resulting from co-occurring mental and physical health conditions. As a result, their care is overseen by numerous professionals spanning various service sectors, but communication between sectors is hindered due to the absence of a standardized assessment system to identify and triage youth to services. A paucity of information surrounding this population's service use lingers and a siloed delivery system persists. Methods: Using archival data collected from 1,020 youth between 12 and 18 years of age, this study explored service complexity among autistic youth with and without psychiatric and medical co-occurring conditions in Ontario, Canada. In doing so, a negative binomial regression was utilized to investigate which predisposing, enabling, and need variables were associated with service complexity. Results: Results revealed that experiencing financial difficulties was not associated with service complexity. However, age, sex, caregiver distress, comorbidity, intellectual disability, and evaluated health status were significant predictors. More specifically, female youth and youth with distressed caregivers had greater mental health service complexity scores. Additionally, youth diagnosed with two or more conditions in addition to ASD who required longer durations of programming, controlling for other predictors, had greater mental health service complexity scores. Yet, youth with an intellectual disability had lower service complexity scores. Discussion: Clinical implications of this study are discussed to inform future investments into mental health efforts for autistic youth.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".