EcoHealth and the Determinants of Health: Perspectives of a Small Subset of Canadian Academics in the EcoHealth Community
Bibliographic record
Abstract
EcoHealth is an emerging field that examines the complex relationships among humans, animals, and the environment, and how these relationships affect the health of each of these domains. The different types of determinants of health greatly influence human health and well-being. Therefore, EcoHealth's ability to improve human, animal, and environmental health and well-being is, in part, influenced by its ability to acknowledge and integrate the determinants of health. However, our previous research demonstrates that the academic EcoHealth literature had a low, uneven engagement with the determinants of health. Accordingly, to make sense of this gap, our research aim is to better understand the views of a small subset of the Canadian EcoHealth community about EcoHealth and the determinants of health relative to EcoHealth. We used a qualitative research design involving seven semi-structured interviews, which were analyzed using thematic analysis. Our findings suggest a tension across themes and a lack of conceptual engagement with the determinants of health. As we consider a future with rapid, unsustainable changes, we expect the identification and integration of the different types of determinants of health within EcoHealth to be imperative for EcoHealth to attain its goal of improving the health and well-being of humans, animals, and the environment.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".