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Record W3128228194 · doi:10.4324/9780429465352-32

Access to Assistive Technology in Canada

2021· book-chapter· en· W3128228194 on OpenAlexaboutno aff
Rosalie H. Wang, Michael Wilson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsAssistive technologyDiversity (politics)Key (lock)Healthy ageingUniversal designSet (abstract data type)Bridge (graph theory)Active ageingGerontologyAgeingBusinessInternet privacyPsychologyPolitical scienceComputer scienceOlder peopleMedicineHuman–computer interactionWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

Assistive technology generally refers to the diversity of technology that can help with daily living and activity participation. Assistive technology provides benefits to people of all ages, including those who are ageing well, ageing into disability, or ageing with a disability. This chapter explores issues around how best to realise the goal of more equitable access to assistive technology in Canada. It is equally concerned with what, exactly, is needed to bridge knowledge, policies, and practices in the ageing and disability fields to arrive at a clearer set of priorities to enable amore integrated system. This chapter includes four key focus areas, starting with an explanation of the importance of assistive technology to both seniors and seniors with disability, and finishing with findings that suggest the current state of access to assistive technology in Canada is patchy. By illustrating key differences in how people ageing into disability and people ageing with disabilities are viewed, this chapter suggests that a solution to some of these issues around inequitable access to assistive technology could be found in integrating the ageing and disability sectors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.109
GPT teacher head0.435
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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