The importance of assistive technology in the productivity pursuits of young adults with disabilities
Bibliographic record
Abstract
BACKGROUND: Young adults with disabilities often use assistive technology (AT) to address personal needs, engage in communities and pursue educational and vocational goals. Little is known about their personal experiences and challenges of accessing and using AT for productivity-related activities. OBJECTIVE: This study aimed to learn from young adults about their experiences and use of AT in supporting their productivity. METHODS: Using a qualitative approach, 20 young adult AT users engaged in semi-structured interviews and a photovoice process. Data were analysed inductively. RESULTS: Three primary themes were identified: I Have to Figure it out Myself, With the Right AT, and Relational Aspects of AT Use. Although participants were experienced AT users, they were often left alone to figure out their emerging needs. They relied on AT to participate in productivity pursuits however stigma around AT use in unsupportive work environments were new concerns. CONCLUSIONS: Young adults with disabilities draw on their experiences of AT use but may need to develop advocacy skills to ensure their needs are met in productivity-related environments. Employers and supervisors should recognize AT as essential to young adult's engagement with productivity-related activities and have an important role in developing inclusive work environments.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".