How Do Interactions Between Early Caregiving Environment and Genes Influence Health and Behavior?
Bibliographic record
Abstract
To promote optimal health and behavioral outcomes in children, nurses have long supported parents in providing the best possible care and nurturance to their offspring. A growing body of neuroscience research argues convincingly for the combined influences of genes and early caregiving on producing an individual's unique health and behavioral phenotype. In this article, we systematically review studies that demonstrate the relationship between qualities of early caregiving and genetic propensity to health and behavioral outcomes. From an initial set of 255 articles, 24 articles met our inclusion criteria. The outcomes fall into four distinct groups: hypothalamic-pituitary-adrenal (HPA) response to stress, externalizing behavior, internalizing behavior, and disorganized attachment. In the articles, authors examined genes that code for the 5-hydroxy tryptamine (serotonin) transporter genes linked polymorphic region [5-HTTLPR] serotonin transporter promoter, D4 dopamine receptor, brain-derived neurotrophic factor, and monoamine oxidase A promoter. The reviewed studies suggest that the effect of the early rearing environment on gene expression relates mainly to HPA response to stress, whereas interactions between genes and caregiving mainly relate to behavior and attachment. Findings have implications for nurses focused on advocacy, prevention, and intervention to support the healthy development of children in families faced with adversity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".