The 'WHO Safe Communities' model for the prevention of injury in whole populations
Notice bibliographique
Résumé
BACKGROUND: The safe communities approach has been embraced around the world as a model for coordinating community efforts to enhance safety and reduce injury. Over 80 communities throughout the world have been formally designated as 'Safe Communities' by the World Health Organization. It is of public health interest to determine to what degree the model is successful, and whether its application does indeed reduce injury rates in communities to which it is introduced. OBJECTIVES: To determine the effectiveness of the Safe Communities model to prevent injury in whole populations, or targeted sub-groups of populations. SEARCH STRATEGY: The search strategy was based on electronic searches, handsearches of selected journals, snowballing from reference lists of selected publications and contacting a key person from each WHO-designated Safe Community. SELECTION CRITERIA: Studies were independently screened for inclusion by two reviewers. Included studies were those conducted within a WHO Safe Community that reported changes in population injury rates within the community compared to a control community. DATA COLLECTION AND ANALYSIS: Data were independently extracted by two reviewers. Meta-analysis was not appropriate, due to the heterogeneity of the included studies. MAIN RESULTS: Only seven WHO Safe Communities, of more than 80 worldwide, have undertaken controlled evaluations using objective sources of injury data. These communities represent only four countries from two geographical regions in the world: the Scandinavian countries of Sweden and Norway and the Pacific nations of Australia and New Zealand. Safe Communities in Sweden and Norway have resulted in significant reductions in injury rates. The Australian and New Zealand communities have been unable to replicate the same level of success. AUTHORS' CONCLUSIONS: Evidence suggests the WHO Safe Communities model is effective in reducing injuries in whole populations. However, important methodological limitations exist in all studies from which evidence can be obtained. A lack of reported detail makes it unclear which factors facilitate or hinder a programme's success, and makes uncertain, whether the success of any particular application of the model is necessarily replicable in other communities. In evaluated programmes that did not report significant decreases in injury rates, this lack of information makes it difficult to distinguish between evidence of no effect of the model, or no evidence of effect. The four countries that have evaluated their Safe Communities with a sufficiently rigorous study design have higher economic wealth and health standards and lower injury rates than much of the world. No evaluations were available from other parts of the world, despite the designation of WHO Safe Communities in countries such as South Africa, Bangladesh, China, Vietnam, Canada, UK and USA. Generalisation of results of studies conducted in just four countries, to the international population needs to be done with caution. There is a need for more high-quality, methodologically strong evaluations of the model in a range of diverse communities and detailed reporting of implementation processes.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,014 |
| Bibliométrie | 0,013 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».