Adaptiveness of emotion regulation flexibility according to long-term implications
Bibliographic record
Abstract
BACKGROUND: The ability to consider the long-term implications of emotional events is integral to mental health and adaptive psychological functioning. However, it remains unclear whether flexibly synchronizing emotion regulation strategies to the long-term implications of emotional events is associated with adaptive outcomes. METHODS: This ecological momentary assessment study examined how emotion regulation flexibility concerning contextual long-term implications is linked to daily emotional experiences and mental health outcomes. Ninety-eight participants provided 1705 real-time assessments of their perceived long-term implications of ongoing emotional events and reported their use of cognitive change (i.e., reappraisal, benefit-finding, perspective-taking) and attentional deployment strategies (i.e., distraction, refocusing, cognitive avoidance). The adaptiveness of adjusting these strategies based on contextual long-term implications was examined using momentary emotional experiences and measures of psychopathology and well-being as outcomes. RESULTS: Consistent with models of emotion regulation flexibility, participants who aligned their use of cognitive change and attentional deployment strategies with the perceived long-term significance of events reported more positive daily emotional experiences and lower levels of psychopathology. LIMITATIONS: Future work should use experimental and longitudinal designs to establish causal pathways. CONCLUSIONS: These findings underscore the importance of accounting for situational long-term implications when evaluating the adaptiveness of regulatory strategies, thereby adding to the growing body of evidence supporting the contextual nature of emotion regulation.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".