Ecosystem Approaches to Health and Well‐Being: Navigating Complexity, Promoting Health in Social–Ecological Systems
Bibliographic record
Abstract
Ecosystem Approaches to Health (also known as ‘ecohealth’) link population or community health and well‐being with the environment and sustainable development. The approach is based on the understanding that health outcomes emerge from interrelationships within social–ecological systems. The ecohealth approach rests on principles of transdisciplinarity, participation, gender and social equity, systems thinking, sustainability, and research‐to‐action. This paper introduces the emerging field of ecohealth as an approach rooted in systems thinking. The approach will be illustrated with three case studies; an application to interrelated crises of poverty, environmental degradation, and zoonotic disease along the Bishnumati River in Kathmandu, Nepal; improvement of community well‐being in a low‐income informal settlement in Chennai, India; and an ongoing project with the Credit Valley Conservation Authority in Southern Ontario, Canada that is oriented to identifying and communicating relationships among ecosystem services and human health so as to demonstrate the importance of watershed management. These projects are typical of the Anthropocene, in which human systems impact the natural systems upon which they depend. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".