Translating Developmental Origins: Improving the Health of Women and Their Children Using a Sustainable Approach to Behaviour Change
Bibliographic record
Abstract
Theories of the developmental origins of health and disease imply that optimising the growth and development of babies is an essential route to improving the health of populations. A key factor in the growth of babies is the nutritional status of their mothers. Since women from more disadvantaged backgrounds have poorer quality diets and the worst pregnancy outcomes, they need to be a particular focus. The behavioural sciences have made a substantial contribution to the development of interventions to support dietary changes in disadvantaged women. Translation of such interventions into routine practice is an ideal that is rarely achieved, however. This paper illustrates how re-orientating health and social care services towards an empowerment approach to behaviour change might underpin a new developmental focus to improving long-term health, using learning from a community-based intervention to improve the diets and lifestyles of disadvantaged women. The Southampton Initiative for Health aimed to improve the diets and lifestyles of women of child-bearing age through training health and social care practitioners in skills to support behaviour change. Analysis illustrates the necessary steps in mounting such an intervention: building trust; matching agendas and changing culture. The Southampton Initiative for Health demonstrates that developing sustainable; workable interventions and effective community partnerships; requires commitment beginning long before intervention delivery but is key to the translation of developmental origins research into improvements in human health.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".