Parental evaluations of neighbourhood green and play spaces and children's mental health
Bibliographic record
Abstract
Abstract Geographical analyses of mental health problems have been undertaken since at least the 1930s, with such work preoccupied primarily with ecological correlations between the prevalence of mental illness and small‐area variations in socioeconomic status. More recently, parks and other green spaces have emerged as place characteristics of interest for their possible public health significance, as they relate to both physical and mental health, including the psychosocial well‐being of children. In this paper, I consider the relationships between parent‐reported adequacy of neighbourhood play and green spaces and children's psychosocial well‐being, as measured by parental assessments of their children's mental health, as well as scores on the Strengths and Difficulties Questionnaire, a psychometric instrument that is demonstrably effective in the identification of behavioural problems in children and adolescents. The findings reported here are consistent with the notion that access to natural spaces and opportunities to play are associated with a reduced risk for mental health problems. Specifically, risk reduction is suggested for overall behavioural functioning, as well as specific behavioural difficulties related to problems in peer relationships, hyperactivity, and emotional problems, as well as parent‐assessed child mental health status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".