Economic policy and public health: Insights from the history of the Canadian Journal of Public Health
Bibliographic record
Abstract
The nearly 115-year history of the Canadian Journal of Public Health (CJPH) provides an important opportunity to reflect on and learn from our past. In response to an invitation to members of the CJPH Editorial Board to curate historical articles around a theme, we undertook a historical examination of our field's engagement, as gleaned through the pages of CJPH, with economic policy. This was inspired by the now well-established connections among political economic policy, population well-being, and health equity. Our analysis of six historical volumes (1917, 1933, 1941, 1961, 1995, and 2013) led to three key findings. First, we found only a slim historical foundation for public health engagement with the economy overall. Second, we observed a strong and seemingly subconscious allegiance to dominant economic paradigms, despite their incompatibility with root causes of health inequities. Third, even though socio-economic inequalities in health are a long-standing preoccupation of CJPH authors, those inequalities are consistently and curiously divorced from their roots in political economic systems. Our findings provide a historical foundation for thinking about how our public health community could be encouraged to engage constructively towards an economic system that supports, rather than obstructs, population well-being and health equity.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.032 | 0.043 |
| Scholarly communication | 0.029 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".