Income-related inequalities in visual impairment and eye screening services in patients with type 2 diabetes
Bibliographic record
Abstract
We aimed to measure income-related inequalities in visual impairment and use of eye screening services amongst Canadian living with type 2 diabetes, and to examine contribution of various socio-demographic factors to identified income-related inequalities. We used data from the Survey on Living with Chronic Disease in Canada-Diabetes Component 2011 (SLCDC-DM) to derive the relative concentration index (RCI) and decomposition of the RCI. Individuals with lower income tended to have more visual impairment compared with those with higher income. The main contribution to the observed income inequality in visual impairment came from age and marital status. Regarding eye screening services, patients with higher income were more likely to use eye screening and preventive eye screening services. The main contributors to increased use were income, having private health insurance and patient's experience in discussing diabetic complications with health professionals. Identified contributors of income-related inequality should be considered when health and healthcare policies are developed in order to minimize and mitigate the observed inequalities.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".