The Developmental Origins of Health and Disease and Sustainable Development Goals: mapping the way forward
Bibliographic record
Abstract
In this paper, meant to stimulate debate, we argue that there is considerable benefit in approaching together the implementation of two seemingly separate recent developments. First, on the global development agenda, we have the United Nations General Assembly's 2015 finalized list of 17 Sustainable Development Goals (SDGs). Several of the SDGs are related to health. Second, the field of Developmental Origins of Health and Disease (DOHaD) has garnered enough compelling evidence demonstrating that early exposures in life affect not only future health, but that the effects of that exposure can be transmitted across generations - necessitating that we begin to focus on prevention. We argue that implementing the SDGs and DOHaD together will be beneficial in several ways; and will require attending to multiple, complex and multidisciplinary approaches as we reach the point of translating science to policy to impact. Here, we begin by providing the context for our work and making the case for a mutually reinforcing, synergistic approach to implementing SDGs and DOHaD, particularly in Africa. To do this, we initiate discussion via an early mapping of some of the overlapping considerations between SDGs and DOHaD.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.013 | 0.023 |
| 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".