Creating Kinship with Nature and Boosting Well-Being: Testing Two Novel Character Strengths-Based Nature Connectedness Interventions
Bibliographic record
Abstract
Abstract We tested the efficacy of engaging in two novel randomized interventions based on character strengths and engaging with nature on boosting nature connectedness and well-being. In Study 1 (N = 134, international community adults) and Study 2 (N = 106, Canadian university students), participants were tasked with noticing how their character strengths were displayed in nature (CinN intervention). In Study 3 (N = 99, Canadian university students), participants were tasked with using their highest character strength in a new way each day to connect with nature (CSwithN intervention). A no treatment control condition was used for comparison. Both interventions significantly boosted nature connectedness (ds = 0.48, 0.66, 0.67). With respect to well-being, the CSinN intervention significantly boosted transcendent connectedness (ds = 053, 0.57), elevation (d = 0.40), and harmony in life (d = 0.48). The CSwithN intervention also significantly boosted transcendent connectedness (d = 0.43), elevation (d = 0.48), and harmony in life (d = 0.50), along with satisfaction of basic need of relatedness (d = 0.58), flourishing (d = 0.57), satisfaction with life (d = 0.44), and positive affect (d = 0.43). Beneficial effects on nature connectedness and well-being were evident despite there being no significant difference in time spent in nature compared to controls. These findings present a unique contribution to the current literature. To our knowledge, the CSinN and CSwithN interventions are the first interventions developed and tested that incorporate character strengths and engagement with nature with the dual goal of boosting nature connectedness and individual well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".