Education disparities in young people with and without neurodisabilities
Bibliographic record
Abstract
AIM: To examine key outcomes in the education of young people with and without neurodisabilities, and to investigate additional disparities in educational achievement in relation to socio-economic background. METHOD: Data were collected on 2488 Canadian children (age range 10-11y) in 1994 and 1995 from the National Longitudinal Survey of Children and Youth whom were followed for 14 years. We performed separate, discrete-time survival analysis to investigate the effects of having a neurodisability on high school completion, enrolment in post-secondary education (PSE), and PSE completion. RESULTS: The baseline prevalence of neurodisabilities was 12%. Fewer children with neurodisabilities completed high school or enrolled in PSE, compared to children without neurodisabilities, irrespective of parental education. The likelihood that students with neurodisabilities completed PSE differed according to their parents' education: students with neurodisabilities living in less-educated families were about half as likely to complete PSE themselves. INTERPRETATION: Children with neurodisabilities receive less education than children without neurodisabilities. Children from families with low educational attainment appear to be particularly vulnerable. WHAT THIS PAPER ADDS: Twelve per cent of children in Canada aged 10 years to 11 years have a neurodisability. High school completion rate was 70% for children with neurodisabilities versus 94% for children without neurodisabilities. Children with neurodisabilities from less-educated families are particularly vulnerable to lower educational achievement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".