A SECOND LOOK AT PHARMACEUTICAL SPENDING AS DETERMINANTS OF HEALTH OUTCOMES IN CANADA
Bibliographic record
Abstract
Per capita spending on pharmaceutical products has increased substantially in recent decades in Canada. Recent Canadian research by Crémieux et al. concludes that there is a strong statistical relationship between pharmaceutical spending and health outcomes (Health Econ. 2005a; 14: 117, Health Econ. 2005b; 14(2): 107-116). This paper takes a second look at pharmaceutical spending as determinants of health outcomes in Canada. In doing so, it examines the robustness of the findings of Crémieux et al. by considering the appropriateness of the data used and statistical approach utilized. Particular attention is given to the potential for non-stationarity and spurious regression, issues related to unit heterogeneity and the choice of estimators. In contrast with earlier findings, on the whole, no discernable relationship between spending on private or public pharmaceutical products and infant mortality or life expectancy at 65 is observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".