Does Emotion Regulation Help Us Pursue Our Goals? Adaptive and Maladaptive Strategy Use and Everyday Goal Progress
Bibliographic record
Abstract
Research on personal everyday goals (e.g., make friends) has mainly focused on the effectiveness of self-control strategies for making progress.While one reason people experience self-control challenges (e.g., temptations, procrastination) is poorly managed mood, there is a lack of research examining how emotion regulation specifically impacts the progress of everyday goals.Using a longitudinal experience sampling method and daily diary design, 317 participants reported their emotion regulation across 14 different strategies and goal progress over a sevenday period, then at one and three-month follow-ups.A greater use of adaptive emotion regulation strategies positively predicted nightly (between persons) and follow-up goal progress, while a greater use of maladaptive strategies did not.The effectiveness of a strategy generally did not depend on the intensity of negative emotions.Out of 14 strategies, reflection was found to have the largest positive impact, highlighting a particularly effective approach to emotions that facilitates goals.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".