MétaCan
Menu
Retour à la cohorte
Enregistrement W1938718597 · doi:10.1002/14651858.cd003300.pub2

Graduated driver licensing for reducing motor vehicle crashes among young drivers

2004· review· en· W1938718597 sur OpenAlexaffabout
Lisa Hartling, Natasha Wiebe, Kelly Russell, Jackie Petruk, Carla Spinola, Terry P. Klassen

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2004
Typereview
Langueen
Domaine
Thématique
Établissements canadiensCapital District Health AuthorityStollery Children's HospitalAlberta Children's HospitalUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésCINAHLCrashMedicinePoison controlInjury preventionOccupational safety and healthMEDLINEHuman factors and ergonomicsPopulationSuicide preventionIntervention (counseling)Psychological interventionEnvironmental healthComputer scienceNursingPathology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Graduated driver licensing (GDL) has been proposed as a means of reducing crash rates among novice drivers by gradually introducing them to higher risk driving situations. OBJECTIVES: To examine the effectiveness of GDL systems in reducing crash rates of young drivers. SEARCH STRATEGY: Studies were identified through searches of MEDLINE, EMBASE, CINAHL, Healthstar, Web of Science, NTIS Bibliographic Database, TRIS Online, SIGLE, the World Wide Web, relevant conference proceedings, consultation with experts and authors, and reference lists. The search was not restricted by language or publication status. SELECTION CRITERIA: Studies were included if: 1) they compared outcomes pre- and post-implementation of a GDL program within the same jurisdiction, 2) comparisons were made between jurisdictions with and without GDL, or 3) both. Studies had to report at least one objective, quantified outcome. Two reviewers independently screened searches and assessed the full text of potentially relevant studies for inclusion using a standard form. DATA COLLECTION AND ANALYSIS: Data were extracted by one reviewer and checked by a second. Additional data were requested from authors. Results were not pooled due to substantial heterogeneity between studies. Percentage change was calculated for each year after the intervention, using one year prior to the intervention as the baseline rate. Results were adjusted by internal controls. Analyses were stratified by different denominators (population, licensed drivers). Results were calculated for the different crash types (overall, injury, fatal, night-time, alcohol, and those resulting in hospitalization). Results were presented for 16 year-olds alone and all teenage drivers combined. MAIN RESULTS: We included 13 studies evaluating 12 GDL programs that were implemented between 1979 and 1998 in the US (n=7), Canada (3), New Zealand (1), and Australia (1). Programs varied in their restrictions during the intermediate stage: e.g. night curfews (8); limitations of extra passengers (2); roadway restrictions (1). Based on the Insurance Institute for Highway Safety classification scheme, no programs were good, six were acceptable, five were marginal, and one was poor. Reductions in crash rates were seen in all jurisdictions and for all crash types. Among 16 year-old drivers, the median decrease in per population overall crash rates during the first year was 31% (range 26-41%). Per population injury crash rates were similar (median 28%, range 4-43%). Results for all teenage drivers, rates per licensed driver, and rates adjusting for internal controls were generally reduced when comparing within jurisdictions. REVIEWERS' CONCLUSIONS: The existing evidence shows that GDL is effective in reducing the crash rates of young drivers, although the magnitude of the effect is unclear. The conclusions are supported by consistent direction of the findings, and the temporal relationship and plausibility of the association. The reviewers have made recommendations for primary research on GDL (e.g. study methods, standardized reporting of outcomes and results, long-term follow-up). The project has also shown what is needed to carry out systematic reviews of observational studies (e.g. quality assessment instruments).

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,014
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Méta-épidémiologie (sens large), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,014
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0200,004
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0020,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,003

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 Opus0,115
Tête enseignante GPT0,368
Écart entre enseignants0,254 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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

En bref

Citations156
Publié2004
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueCochrane Database of Systematic ReviewsTravaux en français237 207