Implicit Theories of Emotion, Goals for Emotion Regulation, and Cognitive Responses to Negative Life Events
Bibliographic record
Abstract
Why do some people routinely respond to emotional difficulty in ways that foster resilience, while others habitually engage in responses associated with deleterious consequences over time? This study examined relations between emotion controllability beliefs and goals for emotion regulation (ER) with peoples' multivariate profile of cognitive ER strategy use. Cluster analysis classified 481 university students (81% female) as adaptive, maladaptive, or low regulators based on their multivariate profile of engagement in five adaptive and four maladaptive cognitive ER strategies. A discriminant function analysis predicting the multivariate profiles supported that lower emotion controllability beliefs and lower performance-avoidance goals for ER significantly distinguished maladaptive regulators from adaptive regulators. Moreover, lower learning, performance-avoidance, and performance-approach goals for ER significantly distinguished low regulators from maladaptive and low regulators. Taken together, findings support that emotion-related beliefs and goals may help to clarify why some people habitually engage in more adaptive patterns of cognitive ER in response to negative life events than others.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".