Avoidable mortality among First Nations adults in Canada: A cohort analysis.
Bibliographic record
Abstract
BACKGROUND: Avoidable mortality is a measure of deaths that potentially could have been averted through effective prevention practices, public health policies, and/or provision of timely and adequate health care. This longitudinal analysis compares avoidable mortality among First Nations and non-Aboriginal adults. DATA AND METHODS: Data are from the 1991-to-2006 Canadian Census Mortality and Cancer Follow-up Study. A 15% sample of 1991 Census respondents aged 25 or older was linked to 16 years of mortality data. This study examines avoidable mortality among 61,220 First Nations and 2,510,285 non-Aboriginal people aged 25 to 74. RESULTS: During the 1991-to-2006 period, First Nations adults had more than twice the risk of dying from avoidable causes compared with non-Aboriginal adults. The age-standardized avoidable mortality rate (ASMR) per 100,000 person-years at risk for First Nations men was 679.2 versus 337.6 for non-Aboriginal men (rate ratio = 2.01). For women, ASMRs were lower, but the gap was wider. The ASMR for First Nations women was 453.2, compared with 183.5 for non-Aboriginal women (rate ratio = 2.47). Disparities were greater at younger ages. Diabetes, alcohol and drug use disorders, and unintentional injuries were the main contributors to excess avoidable deaths among First Nations adults. Education and income accounted for a substantial share of the disparities. INTERPRETATION: The results highlight the gap in avoidable mortality between First Nations and non-Aboriginal adults due to specific causes of death and the association with socioeconomic factors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".