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Record W4414590940 · doi:10.1016/j.jcjo.2025.09.006

Mental health and vision difficulty in adults: a population-based analysis

2025· article· en· W4414590940 on OpenAlexafffundvenue
Andrew Mihalache, Ryan S. Huang, Chris Zajner, Marko M. Popovic, Eryn Tong, Edward Margolin, Peter J. Kertes, Rajeev H. Muni, Radha P. Kohly

Bibliographic record

VenueCanadian Journal of Ophthalmology · 2025
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsSt. Michael's HospitalSunnybrook Health Science CentreMount Sinai HospitalWestern UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMental healthAnxietyPsychosocialDepression (economics)Vision rehabilitationPsychological interventionMental health care

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate associations between self-reported vision difficulties and mental health diagnoses in adults from a nationally representative sample. DESIGN: A retrospective, cross-sectional population-based analysis. METHODS: Using data from the 2022 National Health Interview Survey, we included adult participants who provided self-reported vision data. The primary outcome was the association between vision difficulty and anxiety or depression diagnoses. Secondary outcomes pertained to symptom severity and receiving medications or therapy. Additional analyses explored the relationships between mental health variables and eyewear use. Multivariable regression models were conducted, adjusting for sociodemographic confounders. RESULTS: A total of 27,640 adults were included, of which 5 210 (19%) reported vision difficulties. Adults self-reporting vision difficulty had higher odds of an anxiety disorder (OR = 2.00, 95% CI = [1.82, 2.19]; p < 0.001) and more severe anxiety symptoms per the GAD-7 (OR = 2.67, 95% CI = [2.44, 2.92]; p < 0.001). Likewise, adults self-reporting vision difficulty had higher odds of depression (OR = 2.31, 95% CI = [2.12, 2.52]; p < 0.001) and more severe depressive symptoms per the PHQ-8 (OR = 2.90, 95% CI = [2.67, 3.15]; p < 0.001). Moreover, self-reported vision difficulty was associated with higher odds of taking medication for a mental health condition (OR = 1.71, 95% CI = [1.35, 2.16]; p < 0.001) and receiving therapy (OR = 1.71, 95% CI = [1.53, 1.91]; p < 0.001). Wearing glasses or contact lenses was largely associated with higher odds of the mental health variables studied. CONCLUSION: There are robust associations between self-reported vision difficulty and psychosocial well-being, with individuals reporting vision difficulty more likely to report greater severity of anxiety or depressive symptoms. Similar associations were observed among individuals who wore glasses or contact lenses. Increased collaboration between eye care and mental health professionals is needed.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.368
Teacher spread0.352 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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