Understanding the transmission of attachment using variable- and relationship-centered approaches
Bibliographic record
Abstract
The interrelations of maternal attachment representations, mother–infant interaction in the home, and attachment relationships were studied in 99 adolescent mothers and their 12-month-old infants. A q-factor analysis was used to identify emergent profiles of mother and infant interaction. Traditional multivariate statistical analyses were complemented by a relationship-based approach utilizing latent class analysis. The results confirmed many theoretical predictions linking interaction with autonomous maternal representations and secure attachment, but failed to support a mediating role for maternal sensitivity. Strong associations were found between mothers displaying nonsensitive and disengaged interaction profiles, infants who did not interact harmoniously with the mother and preferred interaction with the visitor, unresolved maternal representations, and disorganized attachment relationships. Moreover, maternal nonsensitive and disengaged interaction in the home mediated the association between unresolved representations and disorganization. The results of the latent class analysis were consistent with these findings and revealed additional, empirically derived associations between attachment classifications and patterns of interactive behavior, some of which prompt a reconsideration of our current understanding of attachment transmission in at-risk populations. This research was supported by a predoctoral fellowship to the first author from the Social Sciences and Humanities Research Council and by research grants to the second and third authors from the Social Sciences and Humanities Research Council, the Ontario Mental Health Foundation, and Health Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".