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Record W2600974565

Socioeconomic Discrepancies in Children’s Accessibility to Health Promoting Resources: An Activity Space Analysis

2015· dissertation· en· W2600974565 on OpenAlexaboutno aff
Lea Ravensbergen-Hodgins

Bibliographic record

VenueTSpace (University of Toronto) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusOverweightEnvironmental healthGeographyHealthy foodPhysical activityBusinessSocioeconomicsPsychologyGerontologyDemographic economicsMedicineObesitySociologyEconomicsPopulation
DOInot available

Abstract

fetched live from OpenAlex

Approximately one third of Canada’s youth are overweight or obese. Children living in low socioeconomic status (SES) households are at greater risk of this condition. Little research examines how mobility and time shape accessibility to food environments and physical activity (PA) facilities, two likely determinants of health expected to vary by SES. Using data from Project BEAT, a study investigating the built environment and Toronto schoolchildren’s active travel, this thesis examines how SES shapes children’s accessibility to food establishments and PA facilities. The activity space construct is used in order to incorporate mobility and time into measures of accessibility. Results indicate that higher SES is associated with greater accessibility and access to PA facilities and lower accessibility to fast food establishments. Furthermore, accessibility to PA facilities and retail food establishments varies over the week. This thesis contributes a more comprehensive understanding of how mobility, time, and SES shape children’s health.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.272
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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
Published2015
Admission routes1
Has abstractyes

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