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Grey spaces: the wheeled fields of residential care

2011· article· en· W2162975622 on OpenAlexafffund
W. Ben Mortenson, John L. Oliffe, William C. Miller, Catherine L. Backman

Bibliographic record

VenueSociology of Health & Illness · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British ColumbiaInstitut Universitaire de Gériatrie de MontréalSimon Fraser University
FundersCanadian Institutes of Health ResearchHealth CanadaMichael Smith Health Research BCCanadian Occupational Therapy Foundation
KeywordsResidential careGerontologyMedicine

Abstract

fetched live from OpenAlex

Many individuals living in residential care use a wheelchair as their primary means of mobility. Although studies have documented challenges encountered by residents in these facilities, few have addressed the role that wheelchairs, as potential enablers and barriers to mobility and participation, play in their lives. To better understand residents' experiences, an ethnographic study was conducted drawing on Bourdieu's theoretical constructs of capital, field, and habitus. Participant observations were conducted at two facilities, and residents, family members and staff took part in in-depth individual interviews. Our analysis revealed three themes. Ready to roll detailed how residents used wheelchairs as a source of comfort and means for expanding their social space, while staff could use them as a means to move and control some residents. Squeaky wheels described how residents solicited assistance from staff and family amid having to wait to perform activities of daily living. In, out and about revealed diversity in the places residents went, spaces they shared and the social activities in which they engaged inside and outside their residential facilities. The study findings emphasise how wheelchairs constitute capital that governs many fields of practice for residents and staff and suggest how practice and policy might be adjusted.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.178
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.397
Teacher spread0.330 · 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 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

Citations23
Published2011
Admission routes2
Has abstractyes

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