Variation in the mu-opioid receptor gene (OPRM1) moderates the influence of maternal sensitivity on child attachment
Bibliographic record
Abstract
The endogenous opioid system is thought to play an important role in mother-infant attachment. In infant rhesus macaques, variation in the μ-opioid receptor gene (OPRM1) is related to differences in attachment behavior that emerges following repeated separation from the mother; specifically, infants carrying at least one copy of the minor G allele of the OPRM1 C77G polymorphism show heightened and more persistent separation distress, as well as a pattern of increased contact-seeking behavior directed towards the mother during reunions (at the expense of affiliation with other group members). Research in adult humans has also linked the minor G allele of the analogous OPRM1 A118G polymorphism with greater interpersonal sensitivity. Adopting an interactionist approach, we examined whether OPRM1 A118G genotype and maternal (in)sensitivity are associated with child attachment style, predicting that children carrying the G allele may be more likely to develop an ambivalent attachment pattern in response to less sensitive maternal care. The sample consisted of 191 mothers participating with their children (n = 223) in the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) project, a community-based, birth cohort study of Canadian mothers and their children assessed longitudinally across the child's development. Maternal sensitivity was coded from at-home mother-child interactions videotaped when the child was 18 months of age. Child attachment was assessed at 36 months using the Strange Situation paradigm. As predicted, G allele carriers, but not AA homozygotes, showed increasing odds of being classified as ambivalently attached with decreasing levels of maternal sensitivity. Paralleling earlier non-human animal research, this work provides support for the theory that endogenous opioids contribute to the expression of attachment behaviors in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".