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Record W1799431681 · doi:10.2105/ajph.2015.302686

Access to Dental Services for People Using a Wheelchair

2015· article· en· W1799431681 on OpenAlexafffundabout
Farnaz Rashid-Kandvani, Belinda Nicolau, Christophe Bedos

Bibliographic record

VenueAmerican Journal of Public Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsWheelchairEnvironmental healthMedicineGerontologyFamily medicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

OBJECTIVES: We investigated the perspectives of people using a wheelchair and their difficulties in accessing dental services. METHODS: Our participatory research was on the basis of a partnership between people using a wheelchair, dental professionals, and academic researchers. Partners were involved in a committee that provided advice at all stages of the project. Our team adopted a qualitative descriptive design. Between October 2011 and October 2012 we conducted semistructured individual interviews with 13 adults who lived in Montreal, Québec, Canada, and used a wheelchair full time. We audio-recorded and transcribed verbatim interviews, and we interpreted data using an inductive thematic analysis. RESULTS: Oral health is of heightened importance to this group of people, who tend to use their mouth as a "third hand." We identified successive challenges in accessing dental services: finding a dentist and being accepted, organizing transportation, entering the building and circulating inside, interacting with the dental staff, transferring and overcoming discomfort on the dental chair, and paying for the treatments. CONCLUSIONS: Governments, dental professional bodies, dental schools, and researchers should work with groups representing wheelchair users to improve access to dental services.

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)0.005
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 score0.419
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.294
GPT teacher head0.542
Teacher spread0.248 · 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

Citations28
Published2015
Admission routes3
Has abstractyes

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