Generalized self-efficacy, positive cognitions, and negative cognitions as mediators of the relationship between conscientiousness and meaning in life.
Bibliographic record
Abstract
Meaning in life (MIL) is a core construct in eudaimonic theories of well-being and an important predictor of physical and psychological health. Although many studies have found that the personality variable conscientiousness has a particular strong relationship to MIL, the mechanisms underlying this relationship have not been examined. The purpose of this study was to test the hypothesis that the conscientiousness–MIL relationship would be mediated by generalized self-efficacy beliefs and, hence, more positive and fewer negative automatic cognitions. Measures of these constructs were administered to 273 college student volunteers recruited from a larger pool of students in classes and via e-mail solicitation, and the model was analyzed using structural equation modelling. As hypothesized, generalized self-efficacy, positive thoughts, and negative thoughts fully mediated the conscientiousness–MIL relationship. The model accounted for 45% of the variance in MIL, 34% of the variance in positive thoughts, 27% of the variance in negative thoughts, and 35% of the variance in generalized self-efficacy. These results suggest that conscientiousness shapes MIL through raising generalized self-efficacy, increasing frequency of positive thoughts, and decreasing frequency of negative thoughts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".