Predicting Service Urgency in Children and Youth with Autism Spectrum Disorder: The Development of an Algorithm
Bibliographic record
Abstract
Background: Autism spectrum disorder (ASD) is often accompanied by various mental health-related symptoms that impact a child’s level of functioning. This variability leads to unequal service urgency needs among children and youth with ASD. However, there is no empirically-based and clinically-informed system that assesses the urgency needs of individuals with ASD seeking mental health services. The current study describes the methods used to develop an algorithm to determine mental health service urgency for children and youth with ASD. Method: Assessments from 20,781 and 53,387 children and youth, drawn from two types of the interRAI Child and Youth Mental Health (ChYMH) instruments used in Ontario, were examined to identify service urgency among those with a probable ASD diagnosis. An interactive decisiontree tool identified rules-based groups that were subsequently assigned to a manageable number of levels using k-means clustering. The fit of the algorithm was assessed by logistic regression analyses. Results: The algorithm used twelve items to predict service urgency among 1,598 children with a probable ASD diagnosis. The decision tree identified 18 groups that were collapsed to five levels, from lowest to highest service urgency. The highest urgency group, which includes 12.1% of children and youth with probable autism and for whom 36.1% are rated urgent, is 11.6 times more likely to be rated as urgent, compared to the lowest group. Conclusions: By implementing the first empirically and clinically supported decision-support tool, appropriate and efficient access to community-based mental health resources can be allocated to children and youth with ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".