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Record W2888971253 · doi:10.1097/opx.0000000000001269

Effectiveness of the Apple iPad as a Spot‐reading Magnifier

2018· article· en· W2888971253 on OpenAlexaffabout
Walter Wittich, Jonathan Jarry, Elliott Morrice, Aaron Johnson

Bibliographic record

VenueOptometry and Vision Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsConcordia UniversityCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre intégré de santé et de services sociaux de la Montérégie-CentreCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré de Santé et de Services Sociaux des LaurentidesUniversité de MontréalSanté MontérégieMAB-Mackay Rehabilitation Centre
Fundersnot available
KeywordsMagnificationComputer scienceReading (process)Assistive technologyUsabilityMultimediaOptometryHuman–computer interactionMedicineComputer vision

Abstract

fetched live from OpenAlex

SIGNIFICANCE: There are no data available comparing the iPad as a portable magnification device with a portable video magnifier. Our study supports the use and integration of mainstream tablet computers into vision rehabilitation to overcome potential barriers to device uptake due to the stigma attached to traditional devices. PURPOSE: Portable personal tablet computers have taken on an important role as assistive devices for individuals with visual impairment; however, their use is rarely supported by independent data. Our study aims to contribute to evidence-based practice by comparing a tablet computer with a portable video magnifier in their use as spot-reading devices. METHODS: We compared the Optelec Compact 5 HD portable video magnifier (Optelec, Longueuil, Canada) and the Apple iPad Air tablet computer (Apple Inc, Cupertino, CA) using the SuperVision+ Magnifier app by asking 60 adults with low vision (age range, 19 to 97 years; mean visual acuity, 20/136) to spot read information on a bill, a medication box, and a food label. Their ability to complete each task was timed; they completed the Quebec User Evaluation of Satisfaction with assistive Technology questionnaire and indicated their preferred device. RESULTS: Performance speed indicated that easier tasks were completed faster; however, there were no statistically significant differences in performance between the two device conditions. The highest satisfaction scores for both devices were identical: dimensions, ease of use, and effectiveness. Preference between the two devices was split at 25 for iPad, 33 for the portable closed-circuit television, and 2 for undecided. CONCLUSIONS: The results indicate that performance speed on our spot-reading tasks was comparable across the two devices. In addition, subjective judgment of the device features and personal preferences lead us to conclude that both the iPad and the portable magnifier may have certain equivalence in their functionality, depending on the user and the task for which they are used.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.041
GPT teacher head0.542
Teacher spread0.501 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2018
Admission routes2
Has abstractyes

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