A vision for the role of governments in supporting the public's health: Learning from the past and expanding our imaginations for the future
Bibliographic record
Abstract
Governments in Canada and elsewhere play a very significant role in shaping the health of populations, but the main ways in which they do so are largely hidden because they lie outside of the health sector and are thus under-leveraged. Neoliberal economic and social policy has eroded upstream determinants of health, with profound consequences for health equity. The current polycrisis-a predictable outcome of neoliberalism-provides an opportunity to re-imagine a role for governments in supporting the public's health. Anchored in a broad version of public health, I consider three levels where we, as a community of health professionals, could start to envision such a version of government, focusing primarily on federal government: (1) public spending; (2) overall orientation of government vis-à-vis the well-being of the population; and (3) the broader political economic paradigm and its dynamics of power. Collectively, these offer opportunity to learn from our past while expanding our imaginations for the future. Such a vision will require the support, and the humility, of healthcare leaders.
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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 | 0.029 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".