Bridging gaps: Assistive technology empowering working adults with disabilities for sustainable development
Bibliographic record
Abstract
Worldwide, an estimated 1.3 billion people of the global population experience significant disabilities. The recent UN Flagship Report on Disability and Sustainable Development Goals emphasizes that the entire achievement of the UN Agenda 2030 requires the active involvement of all individuals, including those with disabilities. The current study explored how Assistive Technology (AT) integration in workplaces contributes to achieving SDG 4 for working adults seeking continuous professional development and the challenges and opportunities in implementing AT in workplaces to support SDG 10. Data were collected from two countries (Malaysia and Canada) using semi-structured interviews. Braun and Clarke's six steps were used to analyze data. Based on the responses, two primary and eleven sub-themes were generated. AT plays a crucial role in developing essential skills in the workplace. It is a transformative resource supporting ongoing professional growth for working adults with disabilities. There are many obstacles to successfully adopting and using AT, including organizational, cultural, technical, and financial ones. Developing partnerships among employers, policymakers, and developers of AT is vital for the sustainable use of these resources.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".