Health intersectoralism in the Sustainable Development Goal era: from theory to practice
Bibliographic record
Abstract
In 2015, the United Nations' (UN) Member States adopted a bold and holistic agenda of the Sustainable Development Goals (SDGs), integrating a vision of peace and prosperity for people and planet. Extensive work within, between, across sectors is required for this bold and holistic agenda to be implemented. It is in this context that this special article collection showcases multisectoral approaches to achieving SDG 3-Good Health and Well-Being-which, though focused explicitly on health, is connected to almost all other goals. A confluence of social and health inequities, within a context of widespread environmental degradation demands systems thinking and intersectoral action. Articles in this issue focus on the SDGs as a stimulus for renewed multisectoral action: processes, policies, and programs primarily outside the health sector, that have health implications through social, commercial, economic, environmental, and political determinants of health. Case studies offer critical lessons on effectively engaging other sectors to enhance their health outputs, identifying co-benefits and 'win-wins' that enhance human 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 | 0.057 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.079 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.046 | 0.058 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".