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Record W2804520758 · doi:10.3390/ijerph15061072

Children and Nature: Linking Accessibility of Natural Environments and Children’s Health-Related Quality of Life

2018· article· en· W2804520758 on OpenAlexaffabout
Suzanne Tillmann, Andrew Clark, Jason Gilliland

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
Fundersnot available
KeywordsContext (archaeology)Quality of life (healthcare)PopulationEnvironmental healthGeographyPublic healthGeospatial analysisPsychologyMedicineCartography

Abstract

fetched live from OpenAlex

A growing body of research suggests that increasing children's nature interactions can have positive benefits for their health-related quality of life (HRQOL); however, researchers have yet to examine how geographical context influences this relationship. The purpose of this study was to examine individual-level and environmental factors that are associated with HRQOL of children from different geographical contexts. Data were collected for 851 children from 34 elementary schools in Ontario, Canada. The natural environments around each child's home were computed using geospatial analyses in a geographic information system. Natural environment measures were combined with HRQOL and the demographics from child surveys to be used in a series of step-wise linear regression models. These models explored the relationship between children's HRQOL and the natural environment in urban/suburban and rural populations. In addition to important individual-level determinants, the findings revealed that characteristics of the natural environment, including the amount of greenness, park, and water, show significant relationships in the urban/suburban population. Interpersonal variables were the key predictors of HRQOL in the rural population. Where children live influences relationships between nature and HRQOL. These findings have implications for policymakers, health practitioners, educators, and parents in the design and the promotion of nature for children's HRQOL.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.385
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations77
Published2018
Admission routes2
Has abstractyes

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