Examining the complex relations between childhood adversity, mindfulness, attachment, and various personality outcomes: A Bayesian structural equation modeling approach.
Bibliographic record
Abstract
Research has demonstrated that childhood abuse and neglect can negatively impact individuals into adulthood. Abuse and neglect are associated with insecure attachment, lower mindfulness, and disordered personality traits, including borderline and psychopathic traits. OBJECTIVE: The purpose of our present study was two-fold; first, we wanted to replicate the finding that the relationships between abuse and neglect and these traits are partly indirect through insecure attachment. Second, we wanted to determine whether mindfulness is an additional mediator in these relationships. METHOD: = 6.5). RESULTS: Findings from a Bayesian Structural Equation Model supported the prediction that the relationship between childhood abuse and neglect and disordered personality traits was indirect through insecure attachment. More specifically, for borderline traits it was indirect through anxious attachment while interpersonal manipulation and callous affect psychopathic traits it was indirect through avoidant attachment. Importantly, mindfulness was not a significant mediator in the model for any of the outcome variables. CONCLUSIONS: Overall, there was support for the idea that insecure attachment was a potential mechanism in the relationship between childhood abuse and neglect and disordered personality traits, though there was no support for mindfulness as a potential mediator. Implications are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".