Parental/Guardians' Connection to Nature Better Predicts Children's Nature Connectedness than Visits or Area-Level Characteristics
Bibliographic record
Abstract
Serious attention and investments are being made by local, regional, and national organizations into policies and programs geared toward reconnecting children with nature to enhance children's well-being and the well-being of the planet. However, this attention and investment commonly focuses on access to, or time in, nature, rather than on nature connectedness, despite evidence consistently supporting the important role that nature connectedness plays in contributing to greater well-being of both humans and the natural environment. A shift in policy efforts toward focusing on enhancing children's nature connectedness may better serve these dual well-being outcomes. Such efforts need to be informed by a greater understanding regarding factors that predict nature connectedness in children. Using data from the Monitor of Engagement with the Natural Environment survey commissioned by Natural England, we assessed child nature connectedness as a function of child, parental/guardians', and area-level characteristics (N = 209 children, N = 209 adults). Children's age, neighborhood deprivation, and green space emerged as significant predictors of child nature connectedness. Parental/guardians' level of nature connectedness, though, emerged as the strongest predictor of children's nature connectedness, even when considered in concert with other child, adult, and area-level characteristics. Our findings provide important information to help guide nature connection initiatives, emphasizing the need for policy and program efforts to move beyond a focus on access and visits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".