Prenatal Identification of Risk for Later Disrupted Parenting Behavior Using Latent Profiles of Childhood Maltreatment
Bibliographic record
Abstract
A history of maltreatment during childhood (e.g., physical and sexual abuse, neglect) can threaten the fundamental human need to form and maintain relationships across development, which ensure safety and security. Furthermore, parental maltreatment history presents considerable risk for the emergence of disrupted parenting behaviors (i.e., contradictory communication, sexualized/role-reversed behavior, disorientation, intrusiveness/negativity, and severe withdrawal), which in turn are associated with children's social-emotional development. The purpose of the present study was to examine whether experiences of childhood maltreatment during pregnancy can predict risk for disrupted parenting behavior before the birth of the child. Given the inherent variability in parenting behaviors, we were interested in how different types or combinations of experiences of maltreatment during childhood are associated with later parenting behaviors. Data were drawn from 120 women from a longitudinal study that spanned from the third trimester of pregnancy through 3-year postpartum. In the current study, mothers' experiences of childhood maltreatment were assessed during pregnancy, and disrupted parenting behaviors were coded from videotaped mother-infant interactions 1-year postpartum. Four profiles of childhood maltreatment were identified using latent profile analysis: low exposure, high exposure, high sexual maltreatment, and high physical and emotional maltreatment. Results revealed that high exposure to multiple types of childhood maltreatment most strongly predicted later disrupted parenting behavior. Women with multiple exposures to different types of maltreatment during childhood may require more intense intervention during pregnancy to prevent risk for the development of disrupted parenting behavior.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".