Prevalence of Pervasive Developmental Disorders in Two Canadian Provinces
Bibliographic record
Abstract
Abstract Although it is generally accepted that the proportion of children diagnosed with pervasive developmental disorders (PDDs) has increased in the past two decades, there is no consensus on the prevalence of these conditions. The accompanying large rise in demand for services, together with uncertainty regarding the extent to which the observed increases are due to a true change in risk, has made PDDs a major public health concern. As few data exist on the prevalence of PDDs in Canada, the aim of this study was to estimate the prevalence of diagnosed PDDs in two Canadian provinces (Manitoba and Prince Edward Island (PEI)) and compare characteristics of diagnosed cases between the two regions. To obtain the estimates, children under the age of 15 years with a PDD diagnosis who lived in either province in 2002 were identified by workers at Children’s Special Services, a provincial government program that supports children with special needs in Manitoba, and by the PEI provincial early intervention coordinator (Department of Social Services and Seniors) and special education autism coordinator (Department of Education). The findings show that the prevalence among children 1–14 years of age was 28.4 per 10,000 (95% confidence interval: 26.1–30.8) in Manitoba and 35.2 per 10,000 (95% confidence interval: 28.2–43.4) in PEI. In Manitoba, children of aboriginal identity with PDDs (8.3%) were significantly underrepresented compared with the general population of aboriginal children living off native reserves (15.6%). Sex ratio, sibling risk, and age at initial diagnosis were similar in the two provinces. These findings can serve as a baseline from which to monitor the prevalence of these conditions over time, providing valuable data for researchers, planners, and service providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".