Arthritis Prevalence and Place of Birth: Findings from the 1994 Canadian National Population Health Survey
Bibliographic record
Abstract
This paper describes the prevalence of arthritis in Canadians by ethnic origin, including Asians, Europeans/Australians, and North American-born Canadians. Data for this study were derived from the 1994 Canadian National Population Health Survey, a cross-sectional survey with a sample of 39,240 persons aged 20 years and older. Arthritis was defined as a long-term health condition of "arthritis or rheumatism" diagnosed by a health professional. Place of birth was determined according to self-reported country of birth. Unconditional multiple logistic regression models were used to adjust for potential confounding effects. The crude prevalence of self-reported arthritis and rheumatism diagnosed by a health professional as a long-term condition for those aged 20 years and older in Canada was 14.2%. The age-sex adjusted prevalence by place of birth was 6.9% in Asians, 14.2% in Europeans/Australians, and 14.5% in North American-born Canadians. In the multivariate analyses using North America-born Canadians as baseline, the risk for arthritis (odds ratio = 0.56) was significantly lower in Asian-born Canadians after adjustment for age, sex, education, income, occupation, and body mass index.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".