Consistent low prevalence of arthritis in quebec: findings from a provincial variation study in Canada based on several canadian population health surveys.
Bibliographic record
Abstract
OBJECTIVE: To examine interprovincial variations of arthritis prevalence focusing on comparisons between Quebec and the rest of Canada. METHODS: Data were derived from the 1991 General Social Survey (GSS), the 1991 Health and Activity Limitation Survey (HALS), and the 1994 and 1996 National Population Health Surveys (NPHS). Arthritis was variously ascertained through self-report of people aged 15 years or older. Prevalence in Quebec was compared with other provinces using extremal quotients (EQ) and correlation analysis. Multiple logistic regression analysis (1996 NPHS) was used to determine whether the low prevalence in Quebec persisted after controlling for confounding factors including age, sex, education, marital status, occupation, body mass index (BMI), comorbidity, and smoking. RESULTS: Quebec consistently had the lowest provincial prevalence of arthritis, with age-sex adjusted prevalences of 18.4% (GSS), 1.9% (HALS), 8.8%, and 10.1% (1994 and 1996 NPHS), which were significantly lower than the corresponding national estimates: 21.2%, 3.1%, 12.9%, and 13.3%. EQ from different surveys varied from 1.5 to 3.0 (significantly > 1). Correlation analyses reveal that relative rankings for provinces were consistent in all surveys. Logistic regression analyses showed a low risk of arthritis for Quebecois: odds ratio 0.75 (95% confidence interval 0.65, 0.87) after controlling for potential confounding factors. CONCLUSION: The low prevalence of arthritis observed in Quebec cannot be explained by potential confounding factors included in the NPHS and warrants further epidemiological studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".