Association of regional racial/cultural context and socioeconomic status with arthritis in the population: A multilevel analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the extent to which differences in individual- and regional-level socioeconomic status and racial/cultural origin account for geographic variations in the prevalence of self-reported arthritis, and to determine whether regional characteristics modify the effect of individual characteristics associated with reporting arthritis. METHODS: Analyses were based on the 2000-2001 Canadian Community Health Survey (>15 years, n = 127,513). Arthritis was self-reported as a long-term condition diagnosed by a health professional. A 2-level logistic regression model was used to identify predictors of reporting arthritis. Individual-level variables included age, sex, income, education, immigration status, racial/cultural origin, smoking, physical activity, and body mass index. Regional-level variables included the proportion of low-income families, low education, unemployment, recent immigrants, Aboriginals, and Asians. RESULTS: At the individual level, age, sex, low income, low education, Aboriginal origin, current smoking, and overweight/obesity were positively associated with reporting arthritis; recent immigration and Asian origin were negatively associated with reporting arthritis. At the regional level, percentages of low-income families and the Aboriginal population were independently associated with reporting arthritis. Regional income and racial/cultural origin moderated the effects of individual income and racial/cultural origin; low-income individuals residing in regions with a higher proportion of low-income families reported arthritis more than low-income individuals living in better-income regions. CONCLUSION: Both individual and regional factors were found to contribute to variations in the prevalence of arthritis, although significant unexplained variation remained. Further research is required to better understand the mechanisms that underlie these regional effects and to identify other contributing factors to the remaining variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".