MétaCan
Menu
Back to cohort
Record W2721485474 · doi:10.1177/0898264317716360

Factors Associated With Visual Impairment and Eye Care Utilization: The International Mobility in Aging Study

2017· article· en· W2721485474 on OpenAlexafffundabout
Safari Joseph Balegamire, Marie‐Josée Aubin, Carmen‐Lucía Curcio, Beatriz Alvarado, Ricardo Oliveira Guerra, Alban Ylli, Nandini Deshpande, Marı́a Victoria Zunzunegui

Bibliographic record

VenueJournal of Aging and Health · 2017
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsQueen's UniversityUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersCanadian Institutes of Health Research
KeywordsEye careOdds ratioVisual impairmentConfidence intervalMedicineDomestic violenceVisual acuityGerontologyEye examinationDemographyInjury preventionPoison controlOptometryPsychiatryOphthalmologyMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine factors associated with visual impairment (VI) and eye care in the International Mobility in Aging Study (IMIAS). METHOD: IMIAS data were analyzed ( N = 1,995 with ages 65-74). Outcomes were VI defined as presenting visual acuity worse than 6/18 in the better eye and eye care utilization assessed by annual visits to eye care professionals. The Hurt-Insult-Threaten-Scream (HITS) questionnaire requested information on domestic violence. RESULTS: Among men, VI varied from 24% in Manizales (Colombia) to 0.5% in Kingston (Canada); among women, VI ranged from 20% in Manizales to 1% in Kingston; lifetime exposure to domestic violence was associated with VI (odds ratio [OR] = 1.87; 95% confidence interval [CI] = [1.17, 3.00]). Eye care utilization varied from 72% in Kingston's men to 25% in Tirana's men; it was associated with domestic violence (prevalence ratio [PR] = 1.3; 95% CI = [1.1, 1.6]). DISCUSSION: VI is more frequent where eye care utilization is low. Domestic violence may be a risk factor for VI.

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 score0.019
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.149
GPT teacher head0.475
Teacher spread0.327 · 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

Citations7
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Aging and HealthSame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207