Enhancing Equitable Access to Assistive Technologies in Canada: Insights from Citizens and Stakeholders
Bibliographic record
Abstract
Les besoins en technologies d'assistance augmentent au Canada, mais l'accès à ces technologies est inégal et fragmentaire, ce qui ferait en sorte que des besoins demeureraient non comblés. Cette étude visait à identifier les valeurs et préférences des citoyens concernant les moyens à utiliser pour favoriser un accès équitable aux technologies d'assistance. Elle visait également à impliquer les décideurs politiques, les parties prenantes et les chercheurs dans des discussions afin d'élaborer des actions dans ce domaine. Au printemps 2017, nous avons organisé trois panels de citoyens et un dialogue avec les parties prenantes. Les principales conclusions des panels ont été incluses dans une synthèse qui a été partagée avec les participants du dialogue. Trente-sept citoyens ont participé aux panels et ont souligné l'importance de l'accès à de l'information fiable, d'un accès équitable aux technologies d'assistance (et ce, quelle que soit la capacité de payer), et de la collaboration. Les vingt-deux participants au dialogue ont fait valoir la nécessité d'un cadre d'orientation pour appuyer l'évolution des pratiques dans l'ensemble au pays. Le cadre d'orientation proposé combinerait des politiques et programmes simplifiés incluant la collecte et l'évaluation de données robustes pour appuyer l'innovation et l'imputabilité à travers le pays. The need for assistive technologies in Canada is increasing, but access is inconsistent and fragmented which can result in unmet needs. We aimed to identify citizens’ values and preferences for how to enhance equitable access to assistive technologies and to engage policymakers, stakeholders, and researchers in deliberations to spark action. In spring 2017, we convened three citizen panels and a stakeholder dialogue. Key panel findings were included in an evidence brief that informed dialogue participants. Thirty-seven citizens participated in panels and emphasized the need for access to reliable information, equitable access to assistive technologies regardless of ability to pay, and the need for collaboration. Twenty-two dialogue participants focused on the need for a guiding framework that supports fundamental change across the country. The proposed policy framework can enhance access to assistive technologies through enabling simplified policies and programs, along with fostering robust data collection and evaluation to support countrywide innovation and accountability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".