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Record W4362512968 · doi:10.22215/etd/2023-15363

Green Space, Physical Activity, and Complete Mental Health: Evidence from the Canadian Longitudinal Study on Aging

2023· dissertation· en· W4362512968 on OpenAlexaffabout
Geneviève Forget

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsMental healthPhysical activityLongitudinal studyRobustness (evolution)PsychologyCovariateGerontologySpace (punctuation)Physical healthUrban green spaceDemographyMedicineEconometricsMathematicsStatisticsComputer sciencePhysical medicine and rehabilitationPsychiatrySociology

Abstract

fetched live from OpenAlex

Bioecological theory (Bronfenbrenner, 1979) suggests that local green space and physical activity may be associated with complete mental health outcomes; however, evidence is mixed.Mixed findings may be attributable to the different operationalizations of variables and/or covariates that have been used.The current study used specification curve analysis to assess the robustness of associations between green space, physical activity, and complete mental health among middle-aged and older adults.Data came from the Canadian Longitudinal Study on Aging and the Canadian Urban Environmental Health Research Consortium (n = 28,635).Green space and physical activity did not interact in most instances.Main effects of green space were mixed (49.71 -75.26%), and median effect sizes were small (β = -0.016,0.025).Main effects of physical activity were robust (89.47 -97.22%), and median effect sizes were small (β = -0.036,0.036).Results highlight that specification decisions influence the strength of associations between variables.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.006
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.127
GPT teacher head0.366
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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