Life course epidemiology and public health
Bibliographic record
Abstract
Life course epidemiology aims to study the effect of exposures on health outcomes across the life course from a social, behavioural, and biological perspective. In this Review, we describe how life course epidemiology changes the way the causes of chronic diseases are understood, with the example of hypertension, breast cancer, and dementia, and how it guides prevention strategies. Life course epidemiology uses complex methods for the analysis of longitudinal, ideally population-based, observational data and takes advantage of new approaches for causal inference. It informs primordial prevention, the prevention of exposure to risk factors, from an eco-social and life course perspective in which health and disease are conceived as the results of complex interactions between biological endowment, health behaviours, social networks, family influences, and socioeconomic conditions across the life course. More broadly, life course epidemiology guides population-based and high-risk prevention strategies for chronic diseases from the prenatal period to old age, contributing to evidence-based and data-informed public health actions. In this Review, we assess the contribution of life course epidemiology to public health and reflect on current and future challenges for this field and its integration into policy making.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".