The psychological health benefits of accepting negative emotions and thoughts: Laboratory, diary, and longitudinal evidence.
Bibliographic record
Abstract
Individuals differ in the degree to which they tend to habitually accept their emotions and thoughts without judging them-a process here referred to as habitual acceptance. Acceptance has been linked with greater psychological health, which we propose may be due to the role acceptance plays in negative emotional responses to stressors: acceptance helps keep individuals from reacting to-and thus exacerbating-their negative mental experiences. Over time, experiencing lower negative emotion should promote psychological health. To test these hypotheses, Study 1 (N = 1,003) verified that habitually accepting mental experiences broadly predicted psychological health (psychological well-being, life satisfaction, and depressive and anxiety symptoms), even when controlling for potentially related constructs (reappraisal, rumination, and other mindfulness facets including observing, describing, acting with awareness, and nonreactivity). Next, in a laboratory study (Study 2, N = 156), habitual acceptance predicted lower negative (but not positive) emotional responses to a standardized stressor. Finally, in a longitudinal design (Study 3, N = 222), acceptance predicted lower negative (but not positive) emotion experienced during daily stressors that, in turn, accounted for the link between acceptance and psychological health 6 months later. This link between acceptance and psychological health was unique to accepting mental experiences and was not observed for accepting situations. Additionally, we ruled out potential confounding effects of gender, ethnicity, socioeconomic status, and life stress severity. Overall, these results suggest that individuals who accept rather than judge their mental experiences may attain better psychological health, in part because acceptance helps them experience less negative emotion in response to stressors. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".