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Active transportation as a way to increase physical activity among children

2009· article· en· W2007713340 on OpenAlexafffundabout
Catherine Morency, Marie‐France Demers

Bibliographic record

VenueChild Care Health and Development · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversité de SherbrookePolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsPhysical activityPsychologyEnvironmental healthTransport engineeringBusinessMedicineEngineeringPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

BACKGROUND: This study examines how active transportation could help increase the daily physical activity volume of school-aged children. METHODS: Using data from the 2003 Origin-Destination Survey carried out among 5% of the 3.5 million residents of the Greater Montreal Area, we determined the proportion of short motorized trips made daily by children 5-14 years old (16 837 children sampled) and estimated the number of steps these trips would account for if they were travelled by foot, taking into account variables such as age, sex and height of children. Modal choice and trip purpose were also examined. RESULTS: In 2003, 31.2% of the daily trips made by children aged 5-14 years in the Greater Montreal Area were 1 km or less (0.6 mile). Of these, 33.0% were motorized trips. Overall, 13.1% of the children in the area had 'steps in reserve', an average of 2238 steps per child per day. If they were performed, these steps would account for 16.6% of the daily recommended volume of physical activity for children. CONCLUSION: Replacing short motorized trips with walking could increase the physical activity level of children and contribute to meet the recommended guidelines, as long as these walking trips add to their daily physical activity volume. It could also reduce their dependence towards adults for moving around.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.305
Teacher spread0.294 · 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

Citations16
Published2009
Admission routes3
Has abstractyes

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