Caregivers' disrupted representations of the unborn child predict later infant–caregiver disorganized attachment and disrupted interactions
Bibliographic record
Abstract
Abstract Infant disorganized attachment is a significant predictor for later psychopathology. The Working Model of the Child Interview (WMCI; C.H. Zeanah, D. Benoit, & M.L. Barton, 1986) elicits and classifies caregivers' perceptions and subjective experience of their child and relationship with the child, which are related to concurrent and future attachment to the caregiver. However, when the WMCI was first developed, the disorganized attachment classification had not been fully developed, so the original WMCI did not include a classification that is linked to disorganized attachment. We adapted the WMCI coding scheme to include items similar to those identified by K. Lyons‐Ruth, E. Bronfman, and E. Parsons (using the Atypical Maternal Behavior Instrument for Assessment and Classification, or AMBIANCE, 1999), which reflect disrupted caregiver behaviors associated with disorganized attachment. This resulted in a new WMCI‐Disrupted (WMCI‐D) scale and classification, disrupted. WMCI‐D was used to code 35 WMCIs administered prenatally. A prenatal disrupted classification was significantly associated with caregiver unresolved classification on the Adult Attachment Interview (M. Main, N. Kaplan, & J. Cassidy, 1985), infant disorganized Strange Situation classification (M.D.S. Ainsworth, M.C. Blehar, E. Waters, & S. Wall, 1978), and disrupted caregiver behaviors toward the infant (using AMBIANCE; K. Lyons‐Ruth et al., 1999), at infant age 12 months. These data suggest WMCI‐D can capture disrupted caregiver internal representations, and identify dyads at risk for disorganized attachment and caregivers with unresolved mourning/trauma. These data also provide evidence for the convergent and predictive validity of the WMCI‐D Scale.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".