Assistive Technology Provision for People with Disabilities in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Abstract Assistive Technology (AT) helps address social and economic barriers and can positively impact the lives of people with disabilities. Single-entry point (SEP) systems have been shown as successful models for reducing barriers encountered when acquiring and using AT. This chapter highlights a mixed method case study in the province of Newfoundland and Labrador (NL), which sought to explore barriers consumers faced in acquiring and being satisfied with AT, as well as the potential for an SEP system in NL. NL is an Atlantic Canadian province characterized by a small population dispersed over a large island and remote mainland. Data were collected using individual interviews with disability service providers in community and post-secondary settings across the province and a survey to assess barriers to accessing AT, AT utilization, and satisfaction among consumers with disabilities. Many consumers and service providers demonstrated that they recognized the benefits of AT but expressed dissatisfaction with existing programs and services citing cost, lack of knowledge, training, and funding subsidies as the most significant barriers to access. Improving access to AT is a necessary step toward enhancing education and employment opportunities, facilitating social inclusion, and optimizing overall health for people with disabilities. Investigating the feasibility of SEP programs modeled after American and Australian initiatives should be part of future planning for Canada, especially in small urban, rural, and remote areas where demand for provision of AT is under-resourced.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".