Assistive technology provision : an assessment of services and supports for people with disabilities in Newfoundland and Labrador
Bibliographic record
Abstract
This study assessed the gaps in provision of assistive technology (AT) services and supports for people with disabilities in Newfoundland and Labrador (NL), and reviewed available evidence on the efficacy of single-entry point systems for future planning and policy change. The mixed method research design included a survey of post-secondary students and adults with vision, hearing, mobility, learning or other disabilities, who are users of AT devices or supports (n = 49), and individual interviews with disability service providers (n =8) in community and post-secondary settings throughout the province. Documents describing existing single-entry point systems and similar programs currently in operation in NL were analyzed as well. Findings indicate that knowledge gaps exist within and between end users or consumers of AT and service providers in community organizations and academic institutions. Although the potential benefit of AT is recognized there is dissatisfaction with the current delivery of AT services and supports for people with various disabilities throughout the province. The need for increased training services for both consumers and service providers was strongly identified. Policy alternatives have been suggested, such as increased coordination of services, a single-entry point system and subsidy programs for AT similar to other provinces in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".