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Record W2949547383 · doi:10.1186/s12889-019-7024-6

The built environment and active transportation safety in children and youth: a study protocol

2019· article· en· W2949547383 on OpenAlexafffund
Brent Hagel, Alison Macpherson, Andrew Howard, Pamela Fuselli, Marie‐Soleil Cloutier, Meghan Winters, Sarah A. Richmond, Linda Rothman, Kathy Belton, Ron Buliung, Carolyn A. Emery, Guy Faulkner, Jacqueline Kennedy, Tracey Ma, Colin Macarthur, Gavin R. McCormack, Greg Morrow, Alberto Nettel‐Aguirre, Liz Owens, Ian Pike, Kelly Russell, Juan Torres, Donald C. Voaklander, Tania Embree, Tate Hubka

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsAcadia UniversityBC Children's HospitalUniversity of British ColumbiaUniversity of AlbertaYork UniversitySickKids FoundationUniversity of TorontoPublic Health OntarioUniversité de MontréalInstitut National de la Recherche ScientifiqueHospital for Sick ChildrenAlberta Children's HospitalInstitute for Clinical Evaluative SciencesSimon Fraser UniversityUniversity of ManitobaParachuteUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineBiostatisticsProtocol (science)Public healthEnvironmental healthOccupational safety and healthPoison controlEpidemiologyInjury preventionHuman factors and ergonomicsSuicide preventionMedical emergencyNursingAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Active transportation, such as walking and biking, is a healthy way for children to explore their environment and develop independence. However, children can be injured while walking and biking. Many cities make changes to the built environment (e.g., traffic calming features, separated bike lanes) to keep people safe. There is some research on how effective these changes are in preventing adult pedestrians and bicyclists from getting hurt, but very little research has been done to show how safe various environments are for children and youth. Our research program will study how features of the built environment affect whether children travel (e.g., to school) using active modes, and whether certain features increase or decrease their likelihood of injury. METHODS: First, we will use a cross-sectional study design to estimate associations between objectively measured built environment and objectively measured active transportation to school among child elementary students. We will examine the associations between objectively measured built environment and child and youth pedestrian-motor vehicle collisions (MVCs) and bicyclist-MVCs. We will also use these data to determine the space-time distribution of pedestrian-MVCs and bicyclist-MVCs. Second, we will use a case-crossover design to compare the built environment characteristics of the site where child and youth bicyclists sustain emergency department reported injuries and two randomly selected sites (control sites) along the bicyclist's route before the injury occurred. Third, to identify implementation strategies for built environment change at the municipal level to encourage active transportation we will conduct: 1) an environmental scan, 2) key informant interviews, 3) focus groups, and 4) a national survey to identify facilitators and barriers for implementing built environment change in municipalities. Finally, we will develop a built environment implementation toolkit to promote active transportation and prevent child pedestrian and bicyclist injuries. DISCUSSION: This program of research will identify the built environment associated with active transportation safety and form an evidence base from which municipalities can draw information to support change. Our team's national scope will be invaluable in providing information regarding the variability in built environment characteristics and is vital to producing evidence-based recommendations that will increase safe active transportation.

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)0.032
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.035
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.016
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0050.004
Science and technology studies0.0080.002
Scholarly communication0.0040.005
Open science0.0050.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0350.009

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 head0.035
GPT teacher head0.335
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations41
Published2019
Admission routes2
Has abstractyes

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