Exploring Cross-Sectoral Implications of the Sustainable Development Goals: Towards a Framework for Integrating Health Equity Perspectives With the Land-Water-Energy Nexus
Bibliographic record
Abstract
Objectives: To assess existing evidence and identify gaps in the integrative framework of the Sustainable Development Goals (SDGs) for their potential to advance cross-sectoral perspectives and actions that connect health equity with the land-water-energy nexus in a watershed context. Methods: Five bibliographic databases were searched from 2016 to 2021. This yielded an initial 226 publications, which were screened for titles, abstracts, and full texts on DistillerSR; resulting in a final 30 publications that were studied. These keywords defined the search terms: “health equity,” “SDGs,” “watershed,” “resource nexus,” and “cross-sectoral.” Results: Thematic syntheses of debates and gaps point to the relevance of the SDGs as a cross-sectoral, integrative platform for place-based programming of the land-water-energy nexus, and to account for negative externalities and cascaded impacts on human and environmental health. Conclusion: For the purpose of monitoring health equity in the contexts of interactions of land, water, and energy in rural, remote, and Indigenous contexts, and on the basis of the SDGs, this paper generates evidence to inform health equity-oriented policies, programs and practices, and to enhance health for equity-seeking populations.
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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 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".