Household income among families with autistic children and youths in Canada: a cross-sectional matched cohort study
Bibliographic record
Abstract
OBJECTIVE: Within the Canadian context, we sought to examine the relationship between households with autistic children/youths and household income. DESIGN: We used data from the 2019 Canadian Health Survey on Children and Youth (CHSCY) to analyse households with a child/youth aged 1-17. Propensity-score matching was used to pair records for children/youths with a reported autism diagnosis to those without. We used linear regression for continuous outcomes (eg, total household income) and Poisson regression for binary outcomes (eg, low household income). All analyses were adjusted for the correlation between matched pairs. PRIMARY OUTCOME MEASURE: Total annual income of all household members. SECONDARY OUTCOME MEASURES: Low household income; single-parent or single-income status; and whether at least one parent was not working or absent from work during the past week. RESULTS: Among a total of 39 951 CHSCY records, we identified a cohort of 815 autistic children/youths. The characteristics of the matched cohort were well-balanced. Households with an autistic child/youth had a mean annual household income that was lower (mean difference: $C16 489; 95% CI $C6384 to $C27 149) compared with matched households without an autistic child/youth. Households with an autistic child/youth were also 26% more likely to be classified as having a low household income (Relative risk (RR) 1.26; 95% CI 1.17 to 1.35) and 20% more likely to rely on a single income (RR 1.20; 95% CI 1.10 to 1.33) compared with households without an autistic child/youth. CONCLUSIONS: Compared with households without an autistic child/youth, those with an autistic child/youth often face more economic challenges, including lower household income and greater risk of food insecurity. Households with an autistic child/youth are more likely to rely on a single income.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".