Does geography matter in mortality? An analysis of potentially avoidable mortality by remoteness index in Canada
Bibliographic record
Abstract
BACKGROUND: The avoidable mortality rate is a key indicator of overall health and health care utilization. However, the avoidable mortality rate may differ by the relative remoteness of a community. Avoidable mortality rates specific to remote areas cannot be investigated unless there is a clear geographic classification of remoteness. Therefore, this research uses a newly developed remoteness index to explore the geographic variability of avoidable mortality in Canada. DATA AND METHODS: The remoteness index, Canadian Vital Statistics-Death Database (2011 to 2015), and the 2016 Census of Population are used to understand the geographic variability of preventable and treatable mortality rates in Canada. Descriptive and multivariate data analysis techniques are used to test the hypothesis that remoteness is one of the statistically significant predictors of avoidable mortality rates in Canada. RESULTS: There is a clear gradient of preventable and treatable mortality rates by relative remoteness. The preventable and treatable mortality rates are significantly higher in more remote areas than in easily accessible areas. The remoteness index is a good predictor of both preventable and treatable causes of mortality for low-Aboriginal census subdivisions but not for high-Aboriginal census subdivisions in Canada. DISCUSSION: Both preventable and treatable mortality rates vary significantly by remoteness, despite Canada's universal health care system. The remoteness of Canadian communities may have affected health care delivery and utilization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| 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".