Understanding the Concept and Practice of Ecosystem Approaches to Health in the Context of Public Health
Bibliographic record
Abstract
A scoping study of the published literature was used to describe the\nconcept and practice of ecosystem approaches to health (ecohealth) in the context of public health. Analysis of commentaries identified 24 themes, expressed in a mind map showing interconnections between themes, with a table of explanations. Most (27 of 29) primary research articles did not explicitly explain how ecohealth was applied in their research, suggesting a need for some standardization in reporting ecohealth. Additionally, a case study approach was undertaken to identify enablers and impediments of ecohealth and how concepts were integrated into a research project of health and environmental sanitation in Vietnam. The project’s conceptual framework was aligned with ecohealth concepts, but in practice, a variety of challenges were identified. In future, ecohealth research teams should include a self-investigation of their ecohealth process to facilitate a comparison of theory-to-practice; this may serve as a best practice for ecohealth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".