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Record W3199365953 · doi:10.1017/s0714980821000210

Assistive Technology Use among Older Adults with Vision Loss: A Critical Discourse Analysis of Canadian Newspapers

2021· article· en· W3199365953 on OpenAlexafffundabout
Katharine Fuchigami, Colleen McGrath, Jordana Bengall, Stephanie Kim, Debbie Laliberté Rudman

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2021
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNewspaperFraming (construction)Assistive technologyLow visionCritical discourse analysisSociologyPsychologyGerontologyMedia studiesPolitical scienceMedicineEngineeringComputer scienceOptometryHuman–computer interaction

Abstract

fetched live from OpenAlex

Low vision assistive devices are often positioned as enabling continued social participation and engagement by older adults in everyday activities; however, previous research suggests that the use of such technologies is restricted by various environmental factors. With little attention previously paid to the discursive environment, this critical discourse analysis critically examined how aging persons with vision loss and assistive technology (AT) were constructed and the occupational possibilities promoted and marginalized through technology use in six Canadian newspapers. In total, 7,289 articles were screened, 1,867 articles underwent a full-text review, and 51 articles were selected for data analysis. Results highlight four key discursive threads related to the framing of disability and AT, positioning of seniors with vision loss, and the ideals and occupations to be attained through AT, and point to the importance of re-configuring discourses addressing AT for seniors with vision loss to expand occupational possibilities and embrace collaborative design approaches.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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 score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.020
GPT teacher head0.326
Teacher spread0.307 · 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

Citations9
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicAssistive Technology in Communication and MobilityFrench-language works237,207