Resiliency, Meaning in Life, and Life Satisfaction: An Examination of Moderating Effects
Bibliographic record
Abstract
While presence of meaning in life (i.e., presence) is associated with a plethora of desirable qualities (e.g., greater well-being, longevity, positive affect), search for meaning is associated with psychological distress (e.g., reports of conflict, rumination, depression; Boyle, Barnes, Buchman, & Bennett, 2009). Individuals with higher resiliency, defined as a multifaceted competency in adapting and recovering from adversity, could potentially mitigate the distress associated with search, and thus, achieve greater satisfaction with life (SWL). The present study examined the moderating role of meaning in life between resiliency (i.e., sense of mastery and sense of relatedness) and SWL in a sample of Canadian university students (N=289). Hierarchical regression analyses showed that there was a positive association between resiliency and SWL and this association was stronger at higher levels compared to lower levels of search for meaning. These results suggest that individuals searching for meaning with high levels of mastery have the greatest SWL, while their counterparts with low mastery have the lowest SWL. Similar moderating effects of search were found with the positive association between sense of relatedness and SWL. Overall, findings suggest that protective factors in resiliency may buffer against the potential negative impact of search.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".