A Meta-Analysis of Time between Maternal Sensitivity and Attachment Assessments: Implications for Internal Working Models in Infancy/Toddlerhood
Bibliographic record
Abstract
This meta-analysis of maternal sensitivity and infant/toddler attachment security includes 41 studies with 2243 dyads. Its purpose is to explore the impact of time between assessments of maternal sensitivity and attachment security on the strength of association between these two constructs. We also examined the interrelationships between this moderator variable and other moderators identified in the literature, such as age and risk status of the sample. We found an overall effect size of r = .27 linking sensitivity to security. However, time between assessment of sensitivity and attachment security moderates this effect size, such that: (1) effect sizes decrease dramatically as one moves from concurrent to nonconcurrent assessments, and (2) temporally distant assessments are a sufficient condition for small effect size; that is, if the time between assessments is large, then a relatively small effect size linking sensitivity and attachment is certain. We also found that time between sensitivity and attachment assessments may account for earlier findings indicating that effect sizes linking sensitivity to security differ according to age of child and sample risk status. Findings are discussed in terms of internal working models and environmental stability.
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 | 0.034 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".