PW 2239 A review of study designs in childhood unintentional injury prevention research in the published literature
Notice bibliographique
Résumé
Injury prevention research draws from many professional disciplines, with papers on the prevention of injury published in hundreds of different journals. This study examined the range of research designs used in recently published work related to child unintentional injury prevention. Hand searches of pertinent journals over the 4 year period 2013–2016 were done to identify empirical, English language, unintentional injury prevention studies in children<19 years. A previously published framework that stratified journals by the average number of injury prevention papers published/year was used to identify and randomly select 10 journals from each stratum to provide a representative sample. Studies were coded by study design, injury type, primary outcome, and type of prevention approach (education, engineering or enforcement) There were 369 studies identified; 6% were qualitative, 33% were descriptive and 59% were analytic. Of the analytic studies, 72% were observational (80% cross-sectional, 8% case control/case crossover, 8% cohort, 4% ecological). Only 28% of analytic studies were experimental, which were related primarily to transportation (80%). Transportation represented 83% of randomized control trials. The majority of studies were related to transportation (60%), all injury (14%) and burns (10%). The most common outcomes measured were non- transport injuries/fatalities (31%), reported behaviours (19%), and transport collisions/injuries/fatalities (17%). Most experimental studies evaluated educational interventions (58%). The majority of studies were in journals with impact factors between1–5 (83%), with only 8% in higher impact journals (impact factors 5–10). Recent child unintentional injury prevention research is generally descriptive or observational, cross-sectional studies in lower impact journals. There are few experimental studies evaluating interventions. Transportation studies predominate, where the injury prevention field overlaps with engineering. More rigorous study designs are needed, focusing on engineering and enforcement interventions and addressing different injury topics, to effectively reduce the population burden of childhood injury.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».