Mother–infant cortisol attunement: Associations with mother–infant attachment disorganization
Bibliographic record
Abstract
This study explores the conceptualization of mother-infant cortisol attunement both theoretically and empirically, and its association with mother-infant attachment disorganization. In a community sample (N = 256), disorganization and cortisol were assessed during the Strange Situation Procedure (SSP) at infant age 17 months. Salivary cortisol was collected at baseline, and 20 and 40 min after the SSP. We utilized three statistical approaches: correlated growth modeling (probing a simultaneous conceptualization of attunement), cross-lagged modeling (probing a lagged, reciprocal conceptualization of attunement), and a multilevel model difference score analysis (to examine the pattern of discrepancies in mother-infant cortisol values). Correlated growth modeling revealed that disorganized, relative to organized, dyads had significant magnitude of change over time, such that, among disorganized dyads, as mothers had greater declines in cortisol, infants had greater increases. The difference score analysis revealed that disorganized, relative to organized, dyads had a greater divergence between maternal and infant cortisol values, such that maternal values were lower than infant values. Disorganized attachment status was not significantly associated with attunement when conceptualized as reciprocal and lagged in the cross-lagged model. Findings suggest that mother-infant dyads in disorganized attachment relationships, who are by definition behaviorally misattuned, are also misattuned in their adrenocortical responses.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".