The influence of community well-being on mortality among Registered First Nations people.
Bibliographic record
Abstract
BACKGROUND: Living in a community with lower socioeconomic status is associated with higher mortality. However, few studies have examined associations between community socioeconomic characteristics and mortality among the First Nations population. DATA AND METHODS: The 1991-to-2006 Census Mortality and Cancer Cohort follow-up, which tracked a 15% sample of Canadians aged 25 or older, included 57,300 respondents who self-identified as Registered First Nations people or Indian band members. The Community Well-Being Index (CWB), a measure of the social and economic well-being of communities, consists of income, education, labour force participation, and housing components. A dichotomous variable was used to indicate residence in a community with a CWB score above or below the average for First Nations communities. Age-standardized mortality rates (ASMRs) were calculated for First Nations cohort members in communities with CWB scores above and below the First Nations average. Cox proportional hazards models examined the impact of CWB when controlling for individual characteristics. RESULTS: The ASMR for First Nations cohort members in communities with a below-average CWB was 1,057 per 100,000 person-years at risk, compared with 912 for those in communities with an above-average CWB score. For men, living in a community with below-average income and labour force participation CWB scores was associated with an increased hazard of death, even when individual socioeconomic characteristics were taken into account. Women in communities with below-average income scores had an increased hazard of death. INTERPRETATION: First Nations people in communities with below-average CWB scores tended to have higher mortality rates. For some components of the CWB, effects remained even when individual socioeconomic characteristics were taken into account.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".