The relationship between life regrets and well-being: a systematic review
Bibliographic record
Abstract
Introduction: The aim of the current study is to examine the association between life regret and well-being, through a systematic review. Methods: Four different databases (PsycINFO, Web of Science, ProQuest, Dissertations & Theses Global, and ERIC) were used to source 31 relevant articles, published between 1989 and 2018. Results: We conclude that experiencing greater life regret is associated with negative effects on various aspects of well-being, such as life satisfaction and depressive symptoms. Although the impact of life regret on well-being is suggested to vary across individual differences in lived experience, age- and gender-related findings remain mixed across studies. This inconsistency may be partly due to the varying protective factors and coping mechanisms individuals use, which may mediate the relationship between life regrets and well-being. Protective factors include the degree of engagement or disengagement towards regret reversal, social comparison, appraisal, and interpretation. Discussion: However, these conclusions are not definite, as the measurement of regret and well-being are inconsistent across studies and there is limited diversity in study samples. Moreover, further research including diverse populations and more standardized measures is necessary to strengthen existing links and identify mediators that could serve as modifiable protective factors between life regret and well-being. Systematic Review Registration: https://osf.io/hy7xj.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".