Differential Pathways from Child Maltreatment Types to Insecure Adult Attachment Styles via Psychological and Social Resources: A Bayesian Network Analysis
Bibliographic record
Abstract
Child maltreatment has been linked to insecure adult attachment. However, it is not yet clear how different child maltreatment types are associated with attachment-related anxiety and avoidance in adulthood; and whether resilience against these insecure attachment styles is dependent on risk-specific resources. Therefore, this study explored differential pathways from child maltreatment types to attachment-related anxiety and avoidance in adulthood and examined whether psychological resources (self-esteem) and social resources (perceived social support) show risk-specific effects. An online survey retrospectively assessed experiences of child maltreatment, the level of attachment-related anxiety and avoidance in adulthood, self-esteem, and perceived social support in N = 604 former members of fundamentalist Christian faith communities (mean age = 41.27 years, SD = 12.50; 65.90% female). Cross-sectional data was analyzed using Bayesian network analysis. Only emotional child maltreatment showed direct relationships to insecure adult attachment. Specifically, emotional abuse and emotional neglect were associated with anxious and avoidant adult attachment, respectively. The effects of other child abuse types on adult attachment were mediated through emotional abuse, which indicated patterns of complex traumatization. Self-esteem mediated the effect of emotional abuse on anxious attachment, while perceived social support mediated the effect of emotional neglect on avoidant attachment. Social support was also linked to self-esteem and was therefore also important for individuals with experiences of emotional abuse. This study showed that child maltreatment types and their interactions are meaningfully linked to attachment-related anxiety and avoidance in adulthood. Interventions for survivors of child maltreatment should focus on risk-specific resources to support their resilience.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".