Practice patterns and determinants of wait time for autism spectrum disorder diagnosis in Canada
Bibliographic record
Abstract
Abstract Background Inefficient diagnostic practices for autism spectrum disorder (ASD) may contribute to longer wait times, delaying access to intervention. The objectives were to describe the diagnostic practices of Canadian pediatricians and to identify determinants of longer wait time for ASD diagnosis. Methods An online survey was conducted through the Canadian Paediatric Society’s developmental pediatrics, community pediatrics, and mental health sections. Participants were asked for demographic information, whether they diagnosed ASD, and elements of their diagnostic assessment. A multiple linear regression of total wait time (time from referral to communication of the diagnosis to the family) as a function of practice characteristics was conducted. Results A total of 90 participants completed the survey, of whom 57 diagnosed ASD in their practices (63.3%). Respondents reported varied use of multi-disciplinary teams, with 53% reporting participation in a team. No two identically composed teams were reported. Respondents also had varied use of diagnostic tools, with 21% reporting no use of tools. The median reported total wait for ASD diagnosis time was 7 months (interquartile range 4–12 months). Longer time spent on assessment was the only variable that remained significantly associated with longer wait time in multiple regression (p = 0.002). Use of diagnostic tools did not significantly affect wait time. Conclusion Canadian ASD diagnostic practices vary widely and wait times for these assessments are substantial—7 months from referral to receipt of diagnosis. Time spent on the assessment is a significant determinant of wait time, highlighting the need for efficient assessment practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".