What Do We Know About Improving Employment Outcomes for Individuals with Autism Spectrum Disorder?
Bibliographic record
Abstract
WHY IS THIS AN IMPORTANT ISSUE?An estimated 1 in 86 children are diagnosed with Autism Spectrum Disorder (ASD)1 making it the most commonly diagnosed childhood neurological condition in Canada.2 Adults living with ASD3 have the poorest employment outcomes of those with disabilities. Most earn less than the national minimum hourly wage, endure extended periods of joblessness and frequently shuffle between positions, further diminishing their prospects. These poor employment outcomes result in lower quality of life and often lead to steep economic costs. WHAT DOES THE RESEARCH TELL US?Employment success increases quality of life Employment enhances quality of life, cognitive functioning and overall well-being of persons with ASD by increasing economic self-sufficiency, financial security, independent living, community participation and self-esteem.4, 5, 6 Unfortunately, employment outcomes for those living with ASD are poor; only 25 per cent of adults with ASD are employed, most of this group is considered high-functioning and only six per cent are competitively employed.7, 8 Success in employment is attributable to a combination of individual characteristics, external supports and policy enabling employment opportunities.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".