What Do We Know About Improving Employment Outcomes for Individuals with Autism Spectrum Disorder?
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is the most commonly diagnosed neurological disorder in children. Adults with ASD have some of the poorest employment outcomes in comparison to others with disabilities. While data in Canada is limited, roughly 25 per cent of Americans living with ASD are employed and no more than six per cent are competitively employed. Most earn less than the national minimum hourly wage, endure extended periods of joblessness and frequently shuffle between positions, further diminishing their prospects. Poor employment outcomes result in lower quality of life and often lead to steep economic costs. Governments are wise to pay attention to the poor employment outcomes as the high numbers of children now diagnosed with ASD will become adults in the future in need of employment opportunities. Improving employment outcomes for those living with ASD is an important policy objective. Work opportunities improve quality of life, economic independence, social integration, and ultimately benefit all. Adults with ASD can succeed with the right supports. Fortunately, there are many emerging policy and program options that demonstrate success. This paper conducts a review of studies and provides policy recommendations based on the literature, to help governments identify appropriate policy options. Some key factors are both those that are unique to the individual and the external supports available; namely school, work, and family. For example, factors that contribute to successful employment for people living with ASD may include IQ, social skills and self-determination, but for all, even for the less advantaged, external assistance from schools, employers and family can help. Inclusive special education programs in high school that offer work experiences are critical as are knowledgeable employers who can provide the right types of accommodation and leadership. In the work environment the use of vocational and rehabilitative supports, from job coaching to technology-mediated training are a few of the work related factors that enhance success. Information in this paper provides policy makers with a way to move forward and enhance the current employment situation for those living with ASD ultimately improving quality of life and economic independence.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".