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Enregistrement W2152877906 · doi:10.1542/peds.2014-0600

There and Back Again: Safety and Health on the Journey to School

2014· letter· en· W2152877906 sur OpenAlexaboutno aff
Gilbert C. Liu, Jason A. Mendoza

Notice bibliographique

RevuePEDIATRICS · 2014
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueUrban Transport and Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRecreationTRIPS architectureMedicinePedestrianInjury preventionOccupational safety and healthPoison controlSuicide preventionHuman factors and ergonomicsEnvironmental healthGerontologyTransport engineeringEngineering

Résumé

récupéré en direct d'OpenAlex

Active school transportation (AST) has been associated with children attaining more moderate-to-vigorous physical activity and a healthier BMI.1–3 Similar to Canadian children, US rates of AST declined from 48% in 1969 to 13% in 2009.4 There are many reasons for this decline, but parents report distance (62% of respondents), traffic-related injury (30%), and weather (19%) as the top 3 barriers.5There are many benefits to AST. Children in developed countries are not sufficiently active and obesity remains a global threat.6 Families driving students to school represent 10% to 14% of US traffic during the morning commute,4 and often such trips are less than a half mile. Communities that have invested in infrastructure to promote walking or biking have shown increased property values, improved air quality, reduced urban heat injury, and greater social cohesion.7–9In 2005, the US Congress established a Safe Routes to School (SRTS) program to improve safety on walking and bicycling routes to elementary and middle schools and to encourage travel between home and school using these modes.10 SRTS was apportioned $612 million benefiting 15 000 schools.11 In 2012, Congress discontinued protected SRTS funding. SRTS initiatives must now compete with other requests such as the Transportation Enhancements and Recreational Trails programs, and show evidence of both increased active transportation and reduced collisions.12 Although pedestrian injury is a major consideration for AST, few studies have examined this rare outcome. In this issue, Rothman et al13 examine AST, the built environment, and associations with pedestrian collisions in Toronto.This Canadian study is among the first to reveal that a higher rate of children walking or biking to school has no significant association with traffic-related injury. This is welcome and important evidence for policy makers who have invested in AST and for activists who pursue more widespread AST. Whether this research will convince parents to forego driving their children to school remains to be seen, but these findings should encourage the growing numbers of Canadian families whose children are walking or biking to school. US readers should be especially inspired by AST occurring during Canadian winters!The authors identify opportunities to expand on their research. Foremost is the limitation posed by the cross-sectional design of their study. The authors report 2 nonintuitive findings: both traffic crossing guards and traffic calming were associated with more pedestrian collisions. These findings are not surprising, given the ecological nature of the study. Streets with more pedestrian collisions are prime targets for interventions to calm traffic or for placing public safety personnel such as crossing guards. Interpreting the results of Rothman et al, it is reasonable to conclude that traffic safety improvements and crossing guards were thus appropriately allocated. This example underscores the need to better define causal relationships. You do not want to get rid of fire trucks just because they are more numerous with more severe fires. Similarly, we caution against extrapolating Rothman et al’s findings to give crossing guards and traffic-calming features the budgetary ax. Although rigorous experimental designs proving the effectiveness of AST are difficult and expensive, built-environment interventions have been successfully evaluated by using longitudinal or quasi-experimental designs; furthermore, a solid body of evidence is available to guide strategies to promote walking or biking in adult commuting.A US-based study in New York City with a before and after design informs the directionality of built-environment interventions and school-aged pedestrian collisions. Investigators reported a reduction in pedestrian injury rates during school-travel hours from 8.8 per 10 000 population per year (preintervention) to 4.4 per 10 000 population per year (postintervention) in census tracts that had SRTS interventions.14 School-aged pedestrian injury rates in census tracts without SRTS interventions showed no change.14 This study should provide policy makers with reassurance that SRTS programs likely reduce the risk of child pedestrian collisions.Investigators should aim to obtain finer-level representation of environmental factors, ie, match the built environment to the actual collision location rather than a broad surrounding area. Also, future work needs to incorporate more accurate information on the speed of vehicular traffic and longer and more frequent time periods for assessing rates of AST (>1 day in different seasons), and applying spatial analyses or other analytic models that can address nonindependence of observations arising from phenomena such as subjects grouping by neighborhood.We recognize the many societal changes that have led to more students being driven to school. As parents, we empathize with families who worry about dangerous streets, distracted drivers, and challenging weather conditions that give pause to even letter carriers. When viewed through the lens of child health, AST is an “old school” form of physical activity that more children should adopt to make the daily trek to and from school. Programs to increase AST, such as SRTS, deserve well-designed evaluations that clearly document their contributions to child well-being and safety.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,100

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0030,004
Science ouverte0,0010,003
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0300,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,060
Tête enseignante GPT0,320
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2014
Routes d'admission1
Résumé présentoui

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