Mental health benefits of interactions with nature in children and teenagers: a systematic review
Bibliographic record
Abstract
BACKGROUND: It is commonly believed that nature has positive impacts on children's health, including physical, mental and social dimensions. This review focuses on how accessibility to, exposure to and engagement with nature affects the mental health of children and teenagers. METHODS: Ten academic databases were used to systematically search and identify primary research papers in English or French from 1990 to 1 March 2017. Papers were included for review based on their incorporation of nature, children and teenagers (0-18 years), quantitative results and focus on mental health. RESULTS: Of the 35 papers included in the review, the majority focused on emotional well-being and attention deficit disorder/hyperactivity disorder. Other outcome measures included overall mental health, self-esteem, stress, resilience, depression and health-related quality of life. About half of all reported findings revealed statistically significant positive relationships between nature and mental health outcomes and almost half reported no statistical significance. CONCLUSIONS: Findings support the contention that nature positively influences mental health; however, in most cases, additional research with more rigorous study designs and objective measures of both nature and mental health outcomes are needed to confirm statistically significant relationships. Existing evidence is limited by the cross-sectional nature of most papers.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".