Maternal Genotypes and Mother-Infant Attachment as Moderators of the Association Between the Early Rearing Environment and Cortisol Secretion
Bibliographic record
Abstract
Background: This dissertation examines maternal genotypes and mother-infant attachment as moderators of the association between the early rearing environment and cortisol secretion. Study 1 examines whether DRD2, SLC6A3, and OXTR genes moderate the association between maternal history of care and maternal cortisol secretion. Study 2 examines mother-infant attachment as a moderator of the associations between maternal depressive symptoms and both infant and maternal cortisol secretion. Method: Mothers self-reported their history of care and depressive symptoms at infant age 16 months. At 17 months, mother-infant attachment was assessed in the Strange Situation Procedure (SSP). Salivary cortisol was assessed at baseline and at 20- and 40-minutes post-SSP. Buccal cells were collected for genotyping. Results: Study 1 revealed that maternal history of low care predicts elevated cortisol secretion, but only for mothers with 10-repeat alleles of SLC6A3 or G alleles of OXTR. Study 2 revealed that maternal depressive symptoms predict elevated cortisol secretion, but only for infants and mothers in non-secure attachment relationships. Conclusions: This dissertation enhances our understanding of the complex relations between the early rearing environment and maternal and infant cortisol secretion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".