Emotion Suppression in Borderline Personality Disorder: An Experience Sampling Study
Bibliographic record
Abstract
This study examined the effects of suppressing emotions in the natural environment among individuals who were high (high-BPD; n = 30) and low (low-BPD; n = 39) in borderline personality disorder (BPD) features. Participants responded to prompts from a personal data assistant eight times per day over a four-day period. The first day was a baseline day, followed by instructions to observe emotions on the second day, suppress emotions on the third day, and observe emotions on the fourth day. Findings ran counter to the notion that emotion suppression is a maladaptive emotion regulation strategy for individuals with BPD features, and also contradict some laboratory research in this area. Specifically, high-BPD participants reported higher positive emotions on the suppress day compared with the observe days, and lower urges to engage in impulsive behavior on the suppress day compared with both the baseline and observe days. On the contrary, for low-BPD participants, negative emotions were higher on the suppress day than they were on the observe or baseline days. Overall, findings indicate the need to further examine when and how emotion suppression leads to positive versus negative effects for persons with BPD features.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".