Glaucoma knowledge in a black community in Toronto
Bibliographic record
Abstract
Background: Black people are at high risk of glaucoma. Routine eye exams are the only way of identifying early glaucoma for treatment and blindness prevention. Public education on glaucoma risk factors and detection may be an important public health strategy to encourage those at high risk to have their eyes checked. Purpose: To evaluate glaucoma knowledge (including presentation, risk factors and detection) and associated socio-demographic factors in a black community in Toronto to aid the development of future public education programs. Materials and Methods: Questionnaire surveys from 5 black people centered churches or organizations. Results: The mean age of participants (n=201) was 48.7 years. Most participants reported glaucoma can cause blindness (88.6%) and run in families (76.0%). However, only few participants (4%) correctly reported glaucoma affects peripheral vision. Over 1 in 4 participants (26.7%) reported incorrectly glaucoma always causes eye symptoms. Less than half of the participants knew black people are at higher risk of glaucoma (48.7%) and glaucoma most likely affects people 40 years or older (46.2%). A history of parents or siblings with glaucoma was not associated with better knowledge of three established glaucoma risk factors (40 years or over, African origin and positive family history). Participants 40 years or older did not have greater knowledge than those under 40 that older people are at higher risk of developing glaucoma (prevalence ratio (PR)=0.90, 95% confidence interval (CI) 0.63-1.26). Participants with and without postsecondary education equally reported that eye examinations are the only way to identify glaucoma (PR=1.02, 95% CI 0.72-1.46). Poverty was associated with lower levels of knowledge of the aforementioned three glaucoma risk factors and that glaucoma is identified through eye examinations. Conclusion: Glaucoma knowledge in Toronto’s black community is inadequate. Education about glaucoma presentation, risk factors and detection should be prioritized in this high-risk group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".