Exposure to public natural space as a protective factor for emotional well-being among young people in Canada
Bibliographic record
Abstract
BACKGROUND: Positive emotional well-being is fundamentally important to general health status, and is linked to many favorable health outcomes. There is societal interest in understanding determinants of emotional well-being in adolescence, and the natural environment represents one potential determinant. Psychological and experimental research have each shown links between exposure to nature and both stress reduction and attention restoration. Some population studies have suggested positive effects of green space on various indicators of health. However, there are limited large-scale epidemiological studies assessing this relationship, specifically for populations of young people and in the Canadian context. The objective of this study was to examine the relationship between exposure to public natural space and positive emotional well-being among young adolescent Canadians. METHODS: This cross-sectional study was based upon the Canadian 2009/10 Health Behaviour in School-aged Children Survey with linked geographic information system (GIS) data. Following exclusions, the sample included 17 249 (grades 6 to 10, mostly ages 11 to 16) students from 317 schools. Features of the natural environment were extracted using GIS within a 5 km radius circular buffer surrounding each school. Multilevel logistic regression was used to examine the relationship between the presence of public natural space (features include green and blue spaces such as parks, wooded areas, and water bodies) and students' reports of positive emotional well-being, while controlling for salient covariates and the clustered nature of the data. RESULTS: Over half of Canadian youth reported positive emotional well-being (58.5% among boys and 51.6% among girls). Relationships between measures of natural space and positive emotional well-being were weak and lacked consistency overall, but modest protective effects were observed in small cities. Positive emotional well-being was more strongly associated with other factors including demographic characteristics, family affluence, and perceptions of neighbourhood surroundings. CONCLUSION: Exposure to natural space in youth's immediate living environment may not be a leading determinant of their emotional well-being. The relationship between natural space and positive emotional well-being may be context specific, and thus different for Canadian youth compared to adult populations and those studied in other nations. Factors of the individual context were stronger potential determinants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".