Laying the Foundation for Policy: Measuring Local Prevalence for Autism Spectrum Disorder
Bibliographic record
Abstract
WHY IS THIS AN IMPORTANT ISSUE?Autism Spectrum Disorder (ASD)1 is the most common neurological condition diagnosed in children in Canada. Estimates of prevalence are reported as national numbers but may not reflect local numbers and consequently local needs. Local and provincial ASD prevalence estimates can be used by policy makers to inform local service delivery, resource allocation and future planning.WHAT DOES THE RESEARCH TELL US?ASD prevalence is on the rise Estimates of ASD prevalence in Canada have risen dramatically over the past several decades.2 The reason for the dramatic rise is uncertain and may be a result of a combination of a true rise in incidence, changing diagnostic criteria and increased awareness.3 It has been speculated that Alberta may have higher numbers of persons with ASD due to family in-migration to utilize higher levels of funding for ASD supports compared to other provinces.4 Prior to this study, there were no prevalence estimates for Alberta to assess this theory. A better understanding of Alberta ASD prevalence is critical as these estimates assist policy-makers, clinicians and educators in planning for school supports, adult day programs, employment programs, housing options and other programs essential to enhancing quality of life for individuals living with ASD and their families.
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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 | 0.030 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".