Cause-specific mortality by education in Canada: a 16-year follow-up study.
Bibliographic record
Abstract
BACKGROUND: People with lower levels of education tend to have higher rates of disease and death, compared with people who have higher levels of education. However, because death registrations in Canada do not contain information on the education of the deceased, unlinked vital statistics cannot be used to examine mortality differentials by education. METHODS: This study examines cause-specific mortality rates by education in a broadly representative sample of Canadians aged 25 or older. The data are from the 1991 to 2006 Canadian census mortality follow-up study, which included about 2.7 million people and 426,979 deaths. Age-standardized mortality rates (ASMRs) were calculated by education for different causes of death. Rate ratios, rate differences and excess mortality were also calculated. RESULTS: All-cause ASMRs were highest among people with less than secondary graduation and lowest for university degree-holders. If all cohort members had the mortality rates of those with a university degree, the overall ASMRs would have been 27% lower for men and 22% lower for women. The causes contributing most to that "excess" mortality were ischemic heart disease, lung cancer, chronic obstructive pulmonary disease, stroke, diabetes, injuries (men), and respiratory infections (women). Causes associated with smoking and alcohol abuse had the steepest gradients. INTERPRETATION: A mortality gradient by education was evident for many causes of death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".