An analysis of the relationship between healthcare spending and health outcomes: a data analytics perspective using the theory of production functions
Bibliographic record
Abstract
This research investigates the relationship between per capita spending on healthcare and population health outcomes at the provincial level in Canada using data from 1980 to 2010. The health outcomes examined include life expectancy at birth and at age 65, number of infant deaths, and potential years of life lost from treatable causes, all of which are separated by gender. Using analytics methods as an application of the theory of growth accounting, the study evaluates the performance of the provincial health care systems in terms of their ability to efficiently produce longevity. The study also specifies the categories of healthcare spending which are most influential in determining the efficient production of longevity and measures the contribution of healthcare spending to the determination of infant mortality and deaths from treatable causes. The methods employed include Data Envelopment Analysis, Decision Tree Induction, and Multivariate Adaptive Regression Splines. The results of the analysis point to the fact that Canada’s provinces operate inefficiently in their production of health outcomes and confirm the importance of healthcare spending to determining health outcomes in Canada.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".