Coping and emotion regulation: A conceptual and measurement scoping review.
Bibliographic record
Abstract
The fields of coping and emotion regulation have mostly evolved separately over decades, although considerable overlap exists. Despite increasing efforts to unite them from a conceptual standpoint, it remains unclear whether conceptual similarities translate into their measurement. The main objective of this review was to summarize and compare self-reported measures of coping and emotion regulation strategies. The secondary objective was to examine whether other psychological measures (e.g., resilience) indirectly reflect regulatory strategies' effectiveness, thus representing additionally informative approaches. Results indicated substantial overlap between coping and emotion regulation measures. In both frameworks, two to eight individual strategies were usually captured, but only a third included ≤20 items. Most commonly evaluated strategies were reappraisal/reinterpretation, active coping/problem-solving, acceptance, avoidance, and suppression. Evidence also suggested psychological distress and well-being measures, especially in certain contexts like natural stress experiments, and resilience measures are possible indirect assessments of these regulatory strategies' effectiveness. These results are interpreted in the light of a broader, integrative affect regulation framework and a conceptual model connecting coping, emotion regulation, resilience, psychological well-being and psychological distress is introduced. We further discussed the importance of alignment between individuals, contexts, and strategies used, and provide directions for future research. Altogether, coping and emotion regulation measures meaningfully overlap. Joint consideration of both frameworks in future research would widen the repertoire of available measures and orient their selection based on other aspects like length or strategies covered, rather than the framework only.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".