MétaCan
Menu
Back to cohort

Prevalence of Visual Impairment and Utilization of Rehabilitation Services in the Visually Impaired Elderly Population of Quebec

2002· article· en· W7143939185 on OpenAlexaffabout
Jacques Gresset, Mona Baumgarten

Bibliographic record

VenueOptometry and Vision Science · 2002
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsAssociation for Canadian Studies
Fundersnot available
KeywordsVisual impairmentRehabilitationVisually impairedPopulationEye careVision rehabilitationSample (material)Functional impairment

Abstract

fetched live from OpenAlex

Data on the prevalence of reported visual impairment and on the utilization of rehabilitation services were collected on a sample consisting of 1777 community‐residing people aged at least 65 years. A visual disability was considered to be present if the answer to at least one of the following two questions was positive: Do you have trouble reading ordinary newsprint with glasses (if normally worn)? Do you have trouble clearly seeing the face of someone 12 feet away with glasses (if normally worn)? Prevalence of a reported near disability was 7.6%, prevalence of a reported distance disability was 4.4%, and 3.5% of subjects reported both types of disability. In a subsample of the surveyed population, the positive predictive value was 21% and the negative predictive value was 100%, using moderate or worse visual impairment as the gold standard. Among those answering yes to both questions, 11.4% received services from a rehabilitation center and 10.0% from a nonprofit agency. The utilization rates (adjusted to apply only to those whose visual impairment was confirmed by visual examination) reached 20% for rehabilitation centers and 17.5% for nonprofit agencies. Low utilization of rehabilitation services raises questions concerning the role of general eye care practitioners, community‐based health centers, and rehabilitation centers in the rehabilitative process of the visually impaired elderly.

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 score0.008
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.440
Teacher spread0.414 · 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
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueOptometry and Vision ScienceSame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207