How do we boost employment outcomes for neurodiverse Albertans?
Bibliographic record
Abstract
Despite the supports that have been put in place, Canadians with developmental disabilities (DD) continue to face obstacles in gaining and maintaining employment. In 2012 two out of three Canadians with DD were out of the workforce and not looking for a job. This dismal statistic means that a large number of capable people are chronically unemployed, a situation that leads to poorer quality of life, with accompanying declines in cognitive function and general well-being. For these neurodiverse Canadians, the cascading effects of unemployment include financial insecurity, poor self-esteem, less ability to live independently and lower community participation. For employers, it means that a pool of diverse talent and resources that would benefit their companies is untapped. Of all disabilities, Canadians with DD face the worst employment levels. Educating employers about neurodiversity and incentivising them to make accommodations in hiring practices and in the workplace would go far toward reducing the number of jobless neurodiverse people. As a signatory to the UN Convention on the Rights of Persons with Disabilities, Canada is expected to provide inclusive and accessible job training, education and labour market opportunities. Yet, labour market activation programs, welfare reforms and equality laws have so far failed to make a difference in the unemployment numbers. A recent study reveals that the top three barriers to unemployment for neurodiverse Albertans include employers’ knowledge, attitude, capacity and management practices; a late start to the concept of work among people with DD; and the stigma of their disability. Programs to remedy the situation abound at the federal and provincial levels and lately, the focus has been shifted to employer education initiatives. Much remains to be done, however, and this communiqué offers suggestions for policy changes that may benefit all parties concerned. One policy could entail changing the design of income assistance programs like Assured Income for the Severely Handicapped (AISH) to remove disincentives to work, such as ensuring continued access to important health benefits. Governments could also offer financial incentives such as wage subsidies and tax credits to employers who hire neurodiverse people, as well as provide monetary incentives for neurodiverse Canadians who wish to be self-employed. Training programs could be available for employers to teach them the value of having a diverse workforce, as well as instructing them in how their companies can become inclusive and accessible. Putting the proper supports in place in the early years would assist neurodiverse high-school youth to participate in career planning, work internships and job training. Helping Canada’s neurodiverse population to get and keep jobs provides benefits to the economy in terms of increased GDP, to employers in terms of talent and ability, and to people with DD who will enjoy a higher quality of life, greater self-esteem and reduced stigma and isolation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".