Latent classes in preschoolers’ internal working models of attachment and emotional security: Roles of family risk
Bibliographic record
Abstract
Abstract Children’s relationships inform their internal working models (IWMs) of the world around them. Attachment and emotional security theory (EST) emphasize the importance of parent–child and interparental relationships, respectively, for IWM. The current study examined (a) data-driven classes in child attachment and emotional security IWM, (b) associations between IWM classes and demographic variables, maltreatment, intimate partner violence (IPV), and maternal depressive symptoms, and (c) consistency in attachment and emotional security IWM classes, including as a function of maltreatment, IPV, and maternal depressive symptoms. Participants were 234 preschool-aged children (n= 152 experienced maltreatment andn= 82 had not experienced maltreatment) and their mothers. Children participated in a narrative-based assessment of IWM. Mothers reported demographics, IPV, and maternal depressive symptoms. Latent class analyses revealed three attachment IWM classes and three emotional security IWM classes. Maltreatment was associated with lower likelihood of being in the secure attachment class and elevated likelihood of being in the insecure dysregulated attachment class. Inconsistencies in classification across attachment and emotional security IWM classes were related to maltreatment, IPV, and maternal depressive symptoms. The current study juxtaposes attachment and EST and provides insight into impacts of family adversity on children’s IWM across different family relationships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".