Meaning in Life Mediates the Association between Environmental Engagement and Loneliness
Bibliographic record
Abstract
Although the positive outcomes of human-environment interactions have been established, research examining the motivation between engagement in pro-environmental activities and psychological well-being is limited. In this mixed-methods study, the relationship between pro-environmental engagement, meaning in life, and well-being, including loneliness and depression, were investigated in a sample of 112 young adults in Canada. It was found that engaging in pro-environmental activities was negatively associated with loneliness. This association was mediated by meaning in life (e.g., an intrinsic motive of caring for future generations). In addition, qualitative analyses explored how engaging in pro-environmental activities has a meaningful impact on meaning in life, and on well-being. A thematic analysis generated three unique themes: (1) responsibility to teach the next generation about the environment, (2) deep appreciation for and connection to nature, and (3) renewed agency through self-directed learning. Overall, findings suggest that meaning in life is a core motive that underlies the association between environmental engagement and loneliness. The present study enriched the relationship between pro-environmentalism and well-being with a mixed-methods perspective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".