It is time for a national strategy on equitable access to assistive technology in Canada
Bibliographic record
Abstract
The time has come to develop and implement a Canadian strategy on equitable access to Assistive Technology (AT). AT use has significant health, social, and economic benefits for people with disabilities and older people, and benefits society by assisting to mitigate the most prominent health and social challenges of our time. Our research with citizens (with/without experiences of disabilities or AT use) and system leaders across Canada determined that access is variable and inequitable, with unmet needs, restricted funding, and inefficiencies. Collaboratively, we devised a blueprint, comprising a policy vision, three priority issues to address, principles to underpin policy actions, and short- and long-term priorities, from which to build a strategy. We hope the blueprint sparks action among citizens and health leaders, especially those working across governments, sectors, and communities to promote leadership and create a cross-jurisdictional coalition to elaborate on a national strategy and action plans for moving forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".