The Canadian census mortality follow-up study, 1991 through 2001.
Bibliographic record
Abstract
BACKGROUND: An important step in monitoring progress toward reducing or eliminating inequalities in health is to determine the distribution of mortality rates across various groups defined by education, occupation, income, language, ethnicity, and Aboriginal, visible minority and disability status. This article describes the methods used to link census data from the long-form questionnaire to mortality data, and reports simple findings for the major groups. DATA AND METHODS: Mortality from June 4, 1991 to December 31, 2001 was tracked among a 15% sample of the adult population of Canada, who completed the 1991 census long-form questionnaire (about 2.7 million, including 260,000 deaths). Age-specific and age-standardized mortality rates were calculated across the various groups, as were hazard ratios and period life tables. RESULTS: Compared with people of higher socio-economic status, mortality rates were elevated among those of lower socio-economic status, regardless of whether status was determined by education, occupation or income. The findings reveal a stair-stepped gradient, with bigger steps near the bottom of the socio-economic hierarchy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".