Provincial healthcare expenditures and household spending: Impact on life expectancy trends in Canada
Bibliographic record
Abstract
Life expectancy reflects a multitude of factors and mirrors the cultural, social, economic, and health conditions prevalent in a society. Calculated at birth, life expectancy is the average number of years an individual anticipates living. The focus of this inquiry is to understand the distinctive contributions of public healthcare expenditures and household healthcare costs in individual Canadian provinces and their implications for life expectancy trend. A random effects regression approach to panel data model, which assumes individual differences are random and not correlated with the independent variables, was applied to analyze the relationship between independent variables, public healthcare expenditure, household healthcare spending, , education levels on life expectancy as dependent variable. Data were collected for nine Canadian provinces, grouped according to life expectancy, public healthcare expenditure, household healthcare spending, , and education levels, over 16 years (2007-2022). Results show a positive correlation between household healthcare spending, , and education levels with life expectancy, while there is a negative correlation between public healthcare expenditure and life expectancy. The findings of this study suggest the need for efficient allocation of public health funds, support for household healthcare expenditures, economic growth, and investment in education to improve health outcomes. Policymakers may consider these findings to formulate comprehensive strategies that address the diverse determinants of health and enhance the overall well-being of Canadians. Keywords: life expectancy, public healthcare expenditure, household healthcare spending
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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 | 0.001 | 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) | 0.003 | 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".