Wellbeing in Winter: Testing the Noticing Nature Intervention During Winter Months
Bibliographic record
Abstract
The main objective of this 2-week RCT study was to test the efficacy of the previously developed Noticing Nature Intervention (NNI) to boost wellbeing during winter months. The NNI consists of noticing the everyday nature encountered in one’s daily routine and making note of what emotions are evoked. Community adults (N = 65) were randomly assigned to engage in the NNI or were assigned to one of two control conditions. Paired t-tests revealed significant increases pre- to post-intervention in the NNI group for positive affect (d = 0.43), elevation (d = 0.59), nature connectedness (d = 0.46), and hope agency (d = 0.64), and a marginally significant increase in transcendent connectedness (d = 0.41). No significant pre-post difference emerged for any aspect of wellbeing in the control conditions. Analysis of qualitative findings revealed that negative emotion themes were 2.13 times more likely to be associated with built photos than with nature photos. Feelings of peace, awe, happiness, humbleness, and hope were more likely to be associated with nature photos, while feelings of annoyance, loneliness, curiosity, uncertainty, anger, yearning, and comfortableness were more likely to be associated with built photos. Overall, results indicated that engaging in the NNI can provide a wellbeing boost, even in the cold of winter. This study is the first (to our knowledge) to test any nature-based wellbeing intervention during colder, winter months, and to directly assess the impact of a nature-based wellbeing intervention on levels of hope.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".