Personality and perfectionism as predictors of life satisfaction: The unique contribution of having high standards for others
Bibliographic record
Abstract
Life satisfaction is directly related to positive mental and physical health outcomes. As such, the promotion of life satisfaction is desirable. To facilitate this process, it is beneficial to identify significant predictors of life satisfaction. Although previous research has established that personality is a reliable predictor of life satisfaction, personality is not easily modifiable. In contrast, perfectionism can be effectively adapted with appropriate therapy, leading to decreases in mental illness symptomology. The present study sought to determine if different aspects of perfectionism predicted life satisfaction beyond the influence of personality. A total of 448 online participants (75% female) completed questionnaires assessing life satisfaction, perfectionism, and personality. Results of a hierarchical multiple regression analysis revealed that lower scores on neuroticism (being emotionally stable; p < 0.001) and higher scores on extraversion ( p < 0.001) and conscientiousness ( p = 0.003) significantly predicted life satisfaction. In addition, one aspect of perfectionism, high standards for others ( p = 0.001), positively predicted life satisfaction beyond the influence of personality. We suggest that encouraging individuals to hold others to high standards is an effective strategy that may foster shared goals and achievements, which in turn may improve overall life satisfaction.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".