Narrowing the transmission gap: A synthesis of three decades of research on intergenerational transmission of attachment.
Bibliographic record
Abstract
Twenty years ago, meta-analytic results (k = 19) confirmed the association between caregiver attachment representations and child-caregiver attachment (Van IJzendoorn, 1995). A test of caregiver sensitivity as the mechanism behind this intergenerational transmission showed an intriguing "transmission gap." Since then, the intergenerational transmission of attachment and the transmission gap have been studied extensively, and now extend to diverse populations from all over the globe. Two decades later, the current review revisited the effect sizes of intergenerational transmission, the heterogeneity of the transmission effects, and the size of the transmission gap. Analyses were carried out with a total of 95 samples (total N = 4,819). All analyses confirmed intergenerational transmission of attachment, with larger effect sizes for secure-autonomous transmission (r = .31) than for unresolved transmission (r = .21), albeit with significantly smaller effect sizes than 2 decades earlier (r = .47 and r = .31, respectively). Effect sizes were moderated by risk status of the sample, biological relatedness of child-caregiver dyads, and age of the children. Multivariate moderator analyses showed that unpublished and more recent studies had smaller effect sizes than published and older studies. Path analyses showed that the transmission could not be fully explained by caregiver sensitivity, with more recent studies narrowing but not bridging the "transmission gap." Implications for attachment theory as well as future directions for research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".