Exploring Ecosystems and Health by Shifting to a Regional Focus: Perspectives from the Oceania EcoHealth Chapter
Bibliographic record
Abstract
This article highlights contributions that can be made to the public health field by incorporating "ecosystem approaches to health" to tackle future environmental and health challenges at a regional level. This qualitative research reviews attitudes and understandings of the relationship between public health and the environment and the priorities, aspirations and challenges of a newly established group (the Oceania EcoHealth Chapter) who are attempting to promote these principles. Ten semi-structured interviews with Oceania EcoHealth Chapter members highlighted the important role such groups can play in informing organisations working in the Oceania region to improve both public health and environmental outcomes simultaneously. Participants of this study emphasise the need to elevate Indigenous knowledge in Oceania and the role regional groups play in this regard. They also emphasis that regional advocacy and ecosystem approaches to health could bypass silos in knowledge and disciplinary divides, with groups like the Oceania EcoHealth Chapter acting as a mechanism for knowledge exchange, engagement, and action at a regional level with its ability to bridge the gap between environmental stewardship and public health.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 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".