Preparing for the future of public health: ecological determinants of health and the call for an eco-social approach to public health education
Bibliographic record
Abstract
As a collective organized to address the education implications of calls for public health engagement on the ecological determinants of health, we, the Ecological Determinants Group on Education (cpha.ca/EDGE), urge the health community to properly understand and address the importance of the ecological determinants of the public's health, consistent with long-standing calls from many quarters-including Indigenous communities-and as part of an eco-social approach to public health education, research and practice. Educational approaches will determine how well we will be equipped to understand and respond to the rapid changes occurring for the living systems on which all life-including human life-depends. We revisit findings from the Canadian Public Health Association's discussion paper on 'Global Change and Public Health: Addressing the Ecological Determinants of Health', and argue that an intentionally eco-social approach to education is needed to better support the health sector's role in protecting and promoting health, preventing disease and injury, and reducing health inequities. We call for a proactive approach, ensuring that the ecological determinants of health become integral to public health education, practice, policy, and research, as a key part of wider societal shifts required to foster a healthy, just, and ecologically sustainable future.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".