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Record W3012972398

Walking the dog: Independent mobility's best friend?

2019· article· en· W3012972398 on OpenAlexaffabout
Negin A. Riazi, Lira Yun, Sébastien Blanchette, François Trudeau, Richard Larouche, Mark S. Tremblay, Guy Faulkner

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of LethbridgeUniversité du Québec à Trois-RivièresChildren's Hospital of Eastern OntarioUniversity of British Columbia
Fundersnot available
KeywordsDemographicsPsychological interventionDemographyPhysical activityPsychologyMedicinePhysical therapyNursingSociology
DOInot available

Abstract

fetched live from OpenAlex

Background: Children's independent mobility (CIM) may facilitate physical activity participation. It is important to examine the correlates of CIM to inform future research and interventions. Dog ownership is positively associated with children's physical activity, but few studies have examined the association between dog ownership and CIM. This study examined this relationship in grade 4,5,6 children in Vancouver, BC. Methods: Data came from the Active Transportation and Independent Mobility (ATIM) study. Parents (N = 751) reported their children's CIM, dog ownership, demographics, and their perceptions of the social environment. Linear mixed effects models were conducted assigning school as a random effect. Results: Child mean age was 10.1±0.9 years, 55% were girls, and 23.3% of families reported having a dog. Below child grade in school (?=.682, p

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)0.000
metaresearch head score (Gemma)0.002
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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
Published2019
Admission routes2
Has abstractyes

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