Water, ecology and health: ecosystems as settings for promoting health and sustainability
Bibliographic record
Abstract
Despite the proposed ecological and systems-based perspectives of the settings-based approach to health promotion, most initiatives have tended to overlook the fundamental nature of ecosystems. This paper responds to this oversight by proposing an explicit re-integration of ecosystems within the healthy settings approach. We make this case by focusing on water as an integrating unit of analysis. Water, on which all life depends, is not only an integral consideration for the existing healthy settings (schools, hospitals, workplaces) but also highlights the ecosystem context of health and sustainability. A focus on catchments (also know as watersheds and river basins) exemplifies the scaled and upstream/downstream nature of ecosystems and draws into sharp focus the cross-sectoral and transdisciplinary context of the social and environmental determinants of health. We position this work in relation to the converging agendas of health promotion and ecosystem management at the local, regional and global scales--and draw on evidence from international initiatives as diverse as the WHO Commission on Social Determinants of Health, and the Millennium Ecosystem Assessment. Using water as a vehicle for understanding the systemic context for human wellbeing, health promotion and disease prevention draws inevitable attention to key challenges of scale, intersectoral governance and the complementary themes of promoting resilience and preventing vulnerability. We conclude by highlighting the importance of building individual and institutional capacity for this kind of integration--equipping a new generation of researchers, practitioners and decision-makers to be conversant with the language of ecosystems, capable of systemic thought and focused on settings that can promote both health and sustainability.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.008 | 0.007 |
| 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".