Perinatal Predictors and Mediators of Attachment Patterns in Preschool Children: Exploration of Children’s Contributions in Interactions with Mothers
Bibliographic record
Abstract
Insecure and disorganized attachment patterns in children are linked to poor health outcomes over the lifespan. Attachment patterns may be predicted by variables that influence the quality of children’s interactions with their primary caregivers/parents (usually mothers) such as prenatal and postnatal exposures and the children’s own behaviours in interactions. The purposes of this exploratory study were to examine: (1) prenatal predictors of children’s attachment patterns, and (2) postnatal mediators and moderators of associations between prenatal predictors and children’s attachment patterns, with adjustment for relevant covariates. Mother–child dyads (n = 214) from the longitudinal Alberta Pregnancy Outcomes and Nutrition (APrON) cohort were studied using valid and reliable measures. Hayes’ mediation analysis was employed to determine direct and indirect effects. Mothers’ prenatal cortisol levels directly predicted disorganized (versus organized) child attachment in unadjusted models. Children’s passivity (in adjusted models) and compulsivity (in unadjusted and adjusted models) in parent-child interactions mediated the pathway between mothers’ prenatal cortisol levels and children’s disorganized attachment patterns. Serial mediation analyses revealed that mothers’ cortisol levels predicted their children’s cortisol levels, which predicted children’s compulsivity, and, ultimately, disorganized attachment in both unadjusted and adjusted models. No predictors were correlated with children’s insecure (versus secure) attachment. This exploratory research suggests that prenatal exposure to mothers’ cortisol levels and children’s behavioural contributions to parent–child interaction quality should be considered in the genesis of children’s attachment patterns, especially disorganization. Interventions focused on parent-child interactions could also focus on addressing children’s behavioral contributions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".