Funding employment inclusion for Ontario youth with disabilities: a theoretical cost-benefit model
Bibliographic record
Abstract
Early engagement in employment-related activities is associated with greater lifetime labor force attachment, which correlates with positive health, social, and quality of life outcomes. People with disabilities often require vocational intervention to enter and remain in the workforce and reap the employment-related health and social benefits. Their labor force attachment brings about the added societal-level benefits of increased tax contributions and reduced social assistance funding. Reason and evidence both support the need for early intervention to facilitate young people with disabilities' workforce entry. Based on available evidence and best practices, and in conjunction with expert input, a cost-benefit model was constructed to provide support for public investment in early employment intervention by demonstrating the societal-level benefits that could be projected. Results indicate the potential benefits for investment in early, targeted employment intervention at a societal level. Two personas were crafted to demonstrate the lifetime societal-level impact of investment in intervention for an individual with disabilities. The results provide relevant arguments for advocates, policy makers, program directors, and people entering adulthood with disabilities to understand the benefits of investing in interventions with the goal of long-term public savings.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".