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Record W2994571472 · doi:10.1080/09687599.2019.1696749

Caught in-between: tensions experienced by community mobility scooter users

2019· article· en· W2994571472 on OpenAlexafffund
Sharon Jang, W. Ben Mortenson, Laura Hurd Clarke, R. Lee Kirby

Bibliographic record

VenueDisability & Society · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsDalhousie UniversityInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsLiminalityNegotiationVariety (cybernetics)AmbiguityPsychologySocial psychologySociologyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

The use of mobility scooters is becoming more common due to their effectiveness, social acceptability, and cost. A variety of benefits have been associated with scooter use, including increased mobility, social participation, confidence, and sense of independence; however, scooter users frequently encounter barriers in their communities. This study employed interpretive description to explore the everyday experiences of scooter users as they navigate the social and physical world. Semi-structured interviews were conducted with 20 participants (10 men and 10 women), aged 40 to 86 years. Our analysis identified a single overarching theme, experiencing liminality, which referred to the ambiguous status of mobility scooters and their users. This ambiguity arose from the participants’ a) ambulatory status and perceived cognitive capacity; b) difficulties fitting into the built environment; and c) experiences of negotiating the social environment. We discuss our findings in light of theorizing about liminality and the social model 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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.434
Teacher spread0.352 · 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 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

Citations11
Published2019
Admission routes2
Has abstractyes

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