Multimorbidity prevalence in Canada: a comparison of Northern Territories with Provinces, 2013/14
Bibliographic record
Abstract
Rapid social, economic, and environmental changes in the northern territories of Canada have raised concerns about potentially increasing levels of chronic disease. This concern prompted us to compare multimorbidity prevalence in Canada between the territories and provinces. We analyzed Canadian Community Health Survey data for 2013/14. We defined multimorbidity, the outcome, as having 3 or more chronic conditions and used survey-weighted multivariable logistic regression for comparisons between territories and provinces. We found a prevalence of multimorbidity in Canada of 14.0% (95% CI: 13.6, 14.3). We could not find significant difference in multimorbidity prevalence between the territories and provinces of Canada overall; however, the territories tended to have lower prevalence estimates than provinces for multimorbidity (adj-OR = 0.88; 95% CI: 0.74-1.04). Sensitivity analyses from propensity score analyses had similar conclusions. Effect modification analyses identified lower multimorbidity in territories versus provinces among households without a post-secondary graduate (adj-OR = 0.46; 95% CI: 0.34-0.61 for northern residence), males (adj-OR = 0.71; 95% CI: 0.54-0.93), and ages 12-29 years (adj-OR = 0.63; 95% CI: 0.39-0.99). Caution is needed in interpreting the results in light of representativeness of CCHS in northern populations of Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".