New insight on intergenerational attachment from a relationship-based analysis
Bibliographic record
Abstract
Research on attachment transmission has focused on variable-centered analyses, where hypotheses are tested by examining linear associations between variables. The purpose of this study was to apply a relationship-centered approach to data analysis, where adult states of mind, maternal sensitivity, and infant attachment were conceived as being three components of a single, intergenerational relationship. These variables were assessed in 90 adolescent and 99 adult mother-infant dyads when infants were 12 months old. Initial variable-centered analyses replicated the frequently observed associations between these three core attachment variables. Relationship-based, latent class analyses then revealed that the most common pattern among young mother dyads featured maternal unresolved trauma, insensitive interactive behavior, and disorganized infant attachment (61%), whereas the most prevalent adult mother dyad relationship pattern involved maternal autonomy, sensitive maternal behavior, and secure infant attachment (59%). Three less prevalent relationship patterns were also observed. Moderation analyses revealed that the adolescent-adult mother distinction differentiated between secure and disorganized intergenerational relationship patterns, whereas experience of traumatic events distinguished between disorganized and avoidant patterns. Finally, socioeconomic status distinguished between avoidant and secure patterns. Results emphasize the value of a relationship-based approach, adding an angle of understanding to the study of attachment transmission.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".