Strategies for Supporting Disability-Inclusive Employment in the Future of Work
Bibliographic record
Abstract
PURPOSE: The world of work is rapidly changing and is expected to create new challenges and opportunities for young adults living with disabilities. This study aimed to identify strategies that might enhance inclusion for young people with disabilities as they navigate the future of work. METHODS: Two survey rounds informed by a Delphi approach were completed by 125 participants that included subject matter experts and individuals with lived expertise of a disability. In the first survey, participants were asked about dimensions of the future of work that could affect the employment of young adults living with disabilities and related workplace, community-based, and policy supports that might enhance disability employment inclusion. In the second survey, participants reviewed strategies suggested to promote inclusion in the future of work and ranked them based on their potential impact in supporting the employment of young adults living with disabilities. RESULTS: Six specific challenge areas in the future of work for young adults with disabilities were identified and included the impact of digital technologies, artificial intelligence (AI) in human resource decision-making, digital globalization, populism and attitudes toward workplace inclusivity, climate change, and external shocks accelerating the pace of change. Top-ranked strategies spanned workplace, social policy, and educational settings and involved delivering lifelong skill-building initiatives, enforcing minimum accessibility standards, enhancing employer disability confidence, and protecting employment rights internationally. CONCLUSION: Strategies identified in this study provide solutions to enhance employment inclusion in the future of work. Findings may be utilized by policymakers, employers, and employment service providers as considerations in the design of employment policies and programs for young adults with disabilities that may sustain employment in a shifting work landscape.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".