Income trajectories of families raising a child with a neurodisability
Bibliographic record
Abstract
PURPOSE: To examine household income trajectories of children with and without neurodisability over a period of 6 years. METHOD: We used four cycles of the Canadian National Longitudinal Survey of Children and Youth, a longitudinal study of the development and well-being of Canadian children from birth into adulthood. RESULTS: While household income increased over time for both groups, families of children with neurodisability had consistently lower household income compared to families of children without neurodisability even after controlling for child and family socio-demographic characteristics. The presence of an interaction effect between parent work status and child with neurodisability at baseline indicated that among children whose parent(s) were not working at baseline, household incomes did not differ between children with and without neurodisability. CONCLUSIONS: The association between child with neurodisability and lower household income may not hold for all types of parents', working status is an important consideration.Implications for RehabilitationFindings support the health selection hypothesis that health status shapes diverging economic conditions over time: children with a ND have lower household incomes than children without a ND child across all waves of the Canadian National Longitudinal Survey of Youth.Income gaps did not increase or decrease over time; rehabilitation services and policies must consider the lower average incomes associated with raising a child with a ND.Social assistance support likely plays a key role in closing the gap, especially for non-working 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".