MétaCan
Menu
Back to cohort
Record W4382793932 · doi:10.1080/14473828.2023.2220993

Exploring the implications for occupational therapy in relation to assistive-technology access and use in urban and rural Canadian communities

2023· article· en· W4382793932 on OpenAlexaffabout
M.L. Wilson, Matilde Cervantes-Navarrete, Thomas Mallette, Denise Cloutier, Shannon Freeman, Simon Carroll

Bibliographic record

VenueWorld Federation of Occupational Therapists Bulletin · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsInstitute of AgingUniversity of Northern British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsOccupational therapyThematic analysisContext (archaeology)Qualitative researchIndependent livingAssistive technologyPsychologyPopulationGerontologyNursingMedicineMedical educationSociologyGeographyEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

This study investigated the lived experience of older adults living in urban and rural geographies to identify and describe important contextual differences and clinical implications for occupational therapists. Community-dwelling older adults (N = 42) were recruited for this study which employed semi-structured interviews conducted over 19 months. A thematic analysis was undertaken to develop our qualitative findings. Two themes emerged, (1) Links to occupational therapy (2) Assistive technologies awareness and use. Occupational therapists utilizing a person-centered approach that considers personalities, infrastructure, care partner engagement, and geographical context, are more likely to achieve the best outcomes. Rural older adults are an underserved population, while urban older adults experience infrastructure barriers to engage with assistive technologies. Occupational therapists can serve as advocates along with their clients to improve equitable access to assistive technology for all.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.362
GPT teacher head0.463
Teacher spread0.101 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueWorld Federation of Occupational Therapists BulletinSame topicAssistive Technology in Communication and MobilityFrench-language works237,207