The prevalence and causes of visual impairment among the male homeless population of Montreal, Canada
Bibliographic record
Abstract
Clinical relevance Homeless populations have lower health indicators, including in eye care. Few data exist on the levels and causes of visual impairment in Canadian homeless populations, and none in Montreal.Background This study aims to characterise the causes and levels of visual impairment, as well as eye care services utilisation among the Montreal homeless.Methods Using random sampling, five homeless shelters were selected. In each shelter, 20 participants were randomly selected. After obtaining informed consent, participants completed an ocular examination, which included: presenting visual acuity (pinhole as needed), intraocular pressure, confrontation visual field, dilated fundus examination, post-dilation autorefraction and questionnaire on social determinants of health.Results A total of 95 participants were examined, of which 97.9% were male. The median age was 49 years old (interquartile range 38–56.5). The age-adjusted prevalence of visual impairment (presenting visual acuity <6/12) was 23.6% (95% CI 15.1–32.9) compared to 6.0% in the Canadian population (Z = 77.9, p < 0.0001). With pinhole correction, the prevalence of visual impairment dropped to 5.8% (95% CI 1.7–11.8). Prevalence was 8.2% (95% CI 3.7–15.9) for cataracts, 11.4% (95% CI 5.9–19.7) for glaucoma or suspects and 4.7% (95% CI 1.7–11.9) for diabetic retinopathy. Lastly, 18.9% of participants had an ocular examination within the last year compared to 41.4% in Canada (Z = −4.5, p < 0.0001) and 13.7% had never had a comprehensive eye examination.Conclusions This sample population shows a prevalence of visual impairment which is four times that of the general Canadian population, with highly prevalent uncorrected refractive error, while accessing primary eye care twice less often.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".