At-a-glance - Canadian Chronic Disease Indicators, 2019 – Updating the data and taking into account mental health
Bibliographic record
Abstract
The 2019 edition of the Canadian Chronic Disease Indicators (CCDI) provides recent estimates of the burden of chronic conditions and measures of general health and associated determinants in Canada. Using data from the CCDI and 2017 Canadian Community Health Survey, we explored the relationship between sociodemographic factors and selfreported mental health. Our findings suggest that sex (males vs females: adjusted odds ratio [aOR] = 1.22); age (65-79 vs 35-49 year age group: aOR = 1.48); education (postsecondary graduate vs less than high school: aOR = 1.68); household income adequacy (highest quintile [Q5] vs lowest [Q1]: aOR = 2.25); and immigrant status (recent immigrants vs nonimmigrants: aOR= 2.29) were significantly associated with higher self-reported mental health.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".