Blindness registrations and socioeconomic factors in Canada: an ecologic study
Bibliographic record
Abstract
PURPOSE: To investigate the socioeconomic factors associated with blindness registration in Canada and its regions using an ecologic approach. METHODS: Canadian National Institute for the Blind (CNIB) blindness registration data for 1996 were divided into units of analysis using postal codes and correlated with demographic and socioeconomic information collected by the 1996 Census of Canada. A total of 1250 units were analyzed representing 28,429,519 persons (98.55% of the population of Canada). Six socioeconomic factors were examined using weighted linear multivariate regression analysis: I) Percentage of the population aged 65 years and over; 2) Median household income; 3) Percentage of the population with university education; 4) Percentage of income derived from government transfer payments; 5) Recent immigrants; and 6) Visible minorities (blacks, Chinese, South Asians). Regression models were created for Canada as well as five geographic regions within Canada. RESULTS: For Canada as a whole, blindness registration prevalence was positively correlated with age distribution and percentage of recent immigrants, and negatively correlated with level of government assistance income and percentage ethnic Chinese population. For five regional regression models, the common predictor variables were age distribution, median household income and percentage of the population who are black. None of the regional models produced an identical set of correlations. CONCLUSIONS: Socioeconomic factors associated with blindness registration prevalence varied across different regions. Median household income was the second most common factor after age distribution, suggesting that areas with lower incomes tend to utilize more blind services. Higher blindness registration rates were associated with areas that had a higher percentage of the population who were black. Differences in blindness registration rates may reflect under-utilization of blind services and/or variations in disease and treatment rates in different populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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".