The benefits of citizen science and nature‐noticing activities for well‐being, nature connectedness and pro‐nature conservation behaviours
Bibliographic record
Abstract
Abstract The current biodiversity crisis, extinction of experience of nature and rising concern about people's well‐being and mental health require us to understand the benefits of activities supporting people's engagement with nature. We ran a 1‐week randomised controlled experiment to test the impact of nature‐focussed activities on people's connectedness to nature and well‐being. This project, called ‘Nature Up Close and Personal: A Wellbeing Experiment’ recruited 500 people who completed the pre‐ and post‐participation surveys which included seven psychometric outcome measures. People were randomly assigned to one of six groups. Those in non‐control groups were asked to take part in one 10‐min activity five times over 8 days; this could be done in any place with nature near to them. The activities were as follows: two different citizen science activities, a nature‐noticing activity (asking people to note three good things in nature: 3GTiN) or a combination of citizen science and 3GTiN. Citizen science, 3GTiN and the combination of the two had significant positive effects on nature connectedness, happiness, sense of worthwhile life and satisfaction with life. 3GTiN (alone and in combination with citizen science) had significant positive effects on pro‐nature conservation behaviours. All activities engaged the pathways to nature connectedness. Compared to 3GTiN, people doing citizen science scored lower at engaging with nature through their senses, and feeling calm or joyful, but higher for feeling that they made a difference. The combined activity engaged the pathways to nature connectedness at least as strongly as the highest scoring of citizen science or 3GTiN individually. This shows the potential to intentionally design citizen science to enhance the pathways to nature connectedness. Nature‐based citizen science is more than just a way to gather environmental data: it benefits well‐being and nature connectedness of participants, and (when in combination with noticing nature activities) pro‐nature conservation behaviours. It adds to the range of activities already proven to enhanced human–nature interactions and nature connectedness. Public policy needs to develop a ‘one health’ approach to people's engagement with nature, supporting communities to both notice and monitor everyday biodiversity, recognising that human and nature's well‐being is interdependent. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".