Bridging conceptual “silos”: bringing together health promotion and sustainability governance for practitioners at the landscape scale
Bibliographic record
Abstract
Environmental health issues are examples of “wicked problems” that require cross-sectoral collaboration at the community level, yet health practitioners and environmental stakeholders find it challenging to see how and why they could be working together. Supportive organisations have been identified as the most vital enabler for individual professionals to participate actively in cross-sectoral initiatives. Ability to justify inter-professional cooperation makes it easier for practitioners to gain the necessary approvals within their institutional mandates. This paper introduces a new conceptual framework that bridges health promotion and sustainability governance to facilitate practical cross-sectoral collaboration that targets complex health-related environmental and social-ecological challenges. The proposed framework integrates six concrete overlapping themes linking health promotion and sustainability governance. The framework also highlights examples of areas where the fields could benefit from one another. Moreover, children's environmental health is proposed as a desirable overall outcome and an attractive venue for potential collaboration, because of its critical role in the public health and well-being of future generations. As a determinant of adult health, children's environmental health emphasises the vital interdependencies between health 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 | 0.044 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.052 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.009 |
| 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".