A systematic review of the associations between biodiversity and children's mental health and wellbeing
Bibliographic record
Abstract
There is a growing interest in how exposure to biodiversity influences mental health and wellbeing; however, few studies have focused on children. The aim of this review was to identify studies that used components of biodiversity and children's health outcomes to assess if there were any themes that could be used to inform urban design and understand the mechanisms behind associations. We used a PROSPERO registered protocol to identify eligible studies following pre-defined inclusion criteria. After searching five databases, 25 studies were included in the review. From these articles we extracted data on the biodiversity exposure and mental health and wellbeing outcomes. Five categories of biodiversity exposure were identified, including species diversity (n = 1; 4%), functional diversity (n = 6; 26%), ecological community (n = 9; 36%), green space metrics (n = 4; 16%), and high-level classifications (n = 6; 24%). Children's health and wellbeing were tabulated into seven categories: play (n = 10; 40%), wellbeing (n = 6; 24%), mental health and cognitive functioning (n = 5; 20%), attention deficit hyperactivity disorder (ADHD)-related behaviours (n = 4; 16%), preferences for nature (n = 3; 12%), academic achievement (n = 2; 8%), and restoration (n = 2; 8%). The high heterogeneity of biodiversity and health measures reduced our ability to identify relationships across studies and formally test for an exposure-dose response. Future research that uses standardised and transferable biodiversity measurements at multiple scales, has stronger reporting rigour, greater consideration of potential modifiers, and increased representation of studies from the Majority World are essential for building a stronger evidence base to deliver child-centred biodiverse landscapes.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".