Moderating effects of emotion regulation strategies on borderline personality symptoms in young adults with adverse childhood experiences
Bibliographic record
Abstract
Young adults who have experienced adverse childhood experiences (ACE) are at increased risk for developing symptoms of borderline personality disorder (BPD), yet many young adults with ACE exposures do not develop BPD symptoms. As emotion regulation strategies are implicated in BPD symptoms, this study aims to understand the moderating effects of two emotion regulation strategies, cognitive reappraisal and expressive suppression, regarding the association between ACEs and BPD. Linear regression analyses examined both self-reported cognitive reappraisal and expressive suppression as moderators in the relationship between ACEs and BPD symptoms in a community-based sample of young adults aged 18–25 years old (n = 172). Childhood adversity was examined at different levels of exposure, in terms of no exposure, exposure to a single ACE, and exposure to multiple ACEs. There was no significant moderation by expressive suppression; however, a significant moderation effect was observed between multiple exposures and cognitive reappraisal. The relationship between multiple exposures to ACEs and BPD symptoms was significant at low levels of reappraisal, with the severity of BPD symptoms significantly decreasing at average and higher levels of reappraisal. These results indicate that cognitive reappraisal may act as a protective strategy against experiencing BPD symptoms despite multiple ACE exposures.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".