Changes in health indicator gaps between First Nations and other residents of Manitoba
Bibliographic record
Abstract
BACKGROUND: The Truth and Reconciliation Commission of Canada has called for better reporting of health disparities between First Nations people and other Canadians to close gaps in health outcomes. We sought to evaluate changes in these disparities using indicators of health and health care use over the last 2 decades. METHODS: We used linked, whole-population, administrative claims data from the Manitoba Centre for Health Policy for fiscal years 1994/95 to 1998/99 and 2012/13 to 2016/17. We measured indicators of health and health care use among registered First Nations and all other Manitobans, and compared differences between these groups over the 2 time periods. RESULTS: Over time, the relative gap between First Nations and all other Manitobans widened by 51% (95% confidence interval [CI] 42% to 60%) for premature mortality rate. For potential years of life lost, the gap widened by 54% (95% CI 51% to 57%) among women and by 32% (95% CI 30% to 35%) among men. The absolute gap in life expectancy widened by 3.14 years (95% CI 2.92 to 3.36) among men and 3.61 years (95% CI 3.38 to 3.84) among women. Relative gaps widened by 20% (95% CI 12% to 27%) for ambulatory specialist visits, by 14% (95% CI 12% to 16%) for hospital separations and by 50% (95% CI 39% to 62%) for days spent in hospital, but narrowed by 33% (95% CI -36% to -30%) for ambulatory primary care visits, by 22% (95% CI -27% to -16%) for mammography and by 27% (95% CI -40% to -23%) for injury hospitalizations. INTERPRETATION: Disparities between First Nations and all other Manitobans in many key indicators of health and health care use have grown larger over time. New approaches are needed to address these disparities and promote better health with and for First Nations.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".