Prenatal exposure to green space and mental health in early adolescence: findings from the TRAILS study
Bibliographic record
Abstract
Little is known about whether prenatal green space exposure contributes to mental health later in life. Using data from a Dutch cohort (TRAILS; n=1,476), we assessed associations between prenatal (1989-1991) green space exposure and four mental health outcomes, namely externalizing problems, internalizing problems, tobacco use, and alcohol use, self-reported at eleven-year-old (2001-2002), and mediation of gestational age and birthweight on these associations. In a structural equational model, adolescents with one standard deviation (SD) unit more prenatal green space exposure showed a 0.119 SD (95%CI:0.028,0.210) more externalizing problems in early adolescence. There are two potential explanations for this unexpected positive association. First, controlling for urbanicity attenuated this association to become insignificant, but the degree of attenuation was minor [0.096, (95%CI:-0.003,0.195)]. Second, this unexpected association might be a consequence of changes in green space exposure in the intervening years, namely childhood (from birth to early adolescence), indicating that individuals with increased green space exposure over childhood exhibited fewer externalizing problems in early adolescence. For the prenatal green space-externalizing problems association, we did not observe mediation by gestational age or birthweight. Overall, these findings suggest no beneficial role of prenatal green space on adolescent mental health. Instead, increased green space exposure in childhood may lead to lower externalizing problems in early adolescence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".