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Record W2791636074 · doi:10.1177/0008417417719723

National evaluation of policies governing funding for wheelchairs and scooters in Canada

2018· article· en· W2791636074 on OpenAlexafffundvenueabout
Emma Smith, Lynn Roberts, Mary Ann McColl, Kathleen A. Martin Ginis, William C. Miller

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMcMaster UniversityQueen's UniversityGF Strong Rehabilitation CentreUniversity of British ColumbiaInternational Collaboration On Repair DiscoveriesVancouver Coastal Health Research InstituteVancouver Coastal Health
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBusinessPublic administrationPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Wheelchairs, scooters, and related equipment are essential for the well-being of individuals with limited mobility and impact participation, health, and quality of life. PURPOSE: Our objective was to identify and evaluate policies governing equipment funding for Canadian adults. We reviewed funding legislation and program documentation for adult Canadians (≥18 years of age) covered by their provincial, territorial, or federal health care plan. Documents were obtained online or through administrative staff. Policy evaluation was guided by the Disability Policy Lens from the Canadian Disability Policy Alliance. KEY ISSUES: Coverage ranges from full funding for all individuals within the jurisdiction to programs limited by strict eligibility criteria. Each jurisdiction defines "disability" or "basic/essential need" differently, contributing to further funding disparities. IMPLICATIONS: Funding policies differ substantially across Canada, resulting in unequal access to equipment dependent on province or territory. We identified eligibility, funding, definitions of mobility, repair and replacement, and prescriber requirement benchmarks that represent policy targets for improved access.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.442
GPT teacher head0.535
Teacher spread0.092 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2018
Admission routes4
Has abstractyes

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