Bibliographic record
Abstract
BACKGROUND: Although racial and ethnic disparities in health services utilization and outcomes have been extensively studied in several countries, this issue has received little attention in Canada. We therefore analyzed data from the 2001 Canadian Community Health Survey to compare the use of health services by members of visible minority groups and nonmembers (white people) in Canada. METHODS: Logistic regression was used to compare physician contacts and hospital admissions during the 12 months before the survey and recent cancer screening tests. Explanatory variables recorded from the survey included visible minority status, sociodemographic factors and health measures. RESULTS: Respondents included 7057 members of visible minorities and 114,255 white people for analysis. After adjustments for sociodemographic and health characteristics, we found that minority members were more likely than white people to have had contact with a general practitioner (adjusted odds ratio [OR] 1.28, 95% confidence interval [CI] 1.14-1.42), but not specialist physicians (OR 1.01, 95% CI 0.93-1.10). Members of visible minorities were less likely to have been admitted to hospital (OR 0.83, 95% CI 0.70- 0.98), tested for prostate-specific antigen (OR 0.64, 95% CI 0.52-0.79), administered a mammogram (OR 0.68, 95% CI 0.59-0.80) or given a Pap test (OR 0.47, 95% CI 0.39-0.56). INTERPRETATION: Use of health services in Canada varies considerably by ethnicity according to type of service. Although there is no evidence that members of visible minorities use general physician and specialist services less often than white people, their utilization of hospital and cancer screening services is significantly less.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".