Principles and Challenges of Applying Epigenetic Epidemiology to Psychology
Bibliographic record
Abstract
The interplay of genetically driven biological processes and environmental factors is a key driver of research questions spanning multiple areas of psychology. A nascent area of research focuses on the utility of epigenetic marks in capturing this intersection of genes and environment, as epigenetic mechanisms are both tightly linked to the genome and environmentally responsive. Advances over the past 10 years have allowed large-scale assessment of one epigenetic mark in particular, DNA methylation, in human populations, and the examination of DNA methylation is becoming increasingly common in psychological studies. In this review, we briefly outline some principles of epigenetics, focusing on highlighting important considerations unique to DNA methylation studies to guide psychologists in incorporating DNA methylation into a project. We discuss study design and biological and analytical considerations and conclude by discussing interpretability of epigenetic findings and how these important factors are currently being applied across areas of psychology.
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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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".