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Record W2082350608 · doi:10.1080/09687599.2013.816626

Power mobility and the built environment: the experiences of older Canadians

2013· article· en· W2082350608 on OpenAlexafffundabout
Alexandra Korotchenko, Laura Hurd Clarke

Bibliographic record

VenueDisability & Society · 2013
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsNegotiationExtant taxonAutonomyContext (archaeology)Power (physics)Built environmentPsychologySocial environmentSociologyGerontologyEngineeringPolitical scienceMedicineGeographySocial science

Abstract

fetched live from OpenAlex

In this article, we employ data from qualitative interviews with 15 men and 14 women aged 51–92 to examine older Canadian adults’ experiences of utilizing power wheelchairs and motorized scooters in the context of the built environment. When functioning properly and utilized within accessible spaces, power mobility devices provided many of the participants with the autonomy they desired. However, the features and functionality of power mobility equipment also constrained participants’ abilities to negotiate their surroundings and maintain valued social roles and physical activities. Participants’ experiences of power mobility technology as enabling or disabling were further complicated by the organization of the built environment, as the men and women described encountering various barriers to mobility within both public and private spaces. We discuss our findings in relation to the extant literature concerning the social and spatial construction of disability.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0240.010
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.350
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations35
Published2013
Admission routes3
Has abstractyes

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