More is not always better: Strategies to regulate negative mood induction in women with borderline personality disorder and depressive and anxiety disorders.
Bibliographic record
Abstract
Individuals with borderline personality disorder (BPD) have difficulties regulating emotions, which may be a consequence of using less effective emotion regulation (ER) strategies to lessen the intensity of their negative emotions. It is not yet known whether people with BPD utilize particular ER strategies to modulate specific mood states and if these strategies are different from those used by individuals with depressive and anxiety disorders. In the present study, 90 participants (30 BPD, 30 anxiety and/or depressive disorders, and 30 healthy controls) underwent a mood induction procedure and specified which ER strategies they used and their perceived difficulty regulating mood following induction. Compared with healthy controls, BPD endorsed higher negative mood prior to, immediately following, and 4 min after neutral and negative mood inductions; more maladaptive ER strategies (e.g., rumination); and more perceived difficulty regulating negative mood. Compared with anxiety and/or depressive disorders, BPD endorsed similar ER strategies and subjective difficulty during mood inductions, endorsed higher negative mood following a neutral video and 1 negative video, and recorded higher RSA reactivity during and following 2 negative videos. Results suggest that individuals with BPD use a higher number of maladaptive ER strategies compared with healthy controls, which may lead to less effective modulation of negative mood and higher reports of difficulty regulating emotions. In addition, physiological measurements indicated that individuals with BPD may have higher RSA reactivity in response to negative mood induction compared with other mental disorders, which may reflect inefficient or disorganized attempts to regulate emotional arousal. (PsycINFO Database Record (c) 2018 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".