Resilience-Informed Community Violence Prevention and Community Organizing Strategies for Implementation: Protocol for a Hybrid Type 1 Implementation-Effectiveness Trial
Notice bibliographique
Résumé
BACKGROUND: Community violence is a persistent and challenging public health problem. Community violence not only physically affects individuals, but also its effects reverberate to the well-being of families and entire communities. Being exposed to and experiencing violence are adverse community experiences that affect the well-being and health trajectories of both children and adults. In the United States, community violence has historically been addressed through a lens of law enforcement and policing; the impact of this approach on communities has been detrimental and often ignores the strengths and experiences of community members. As such, community-centered approaches to address violence are needed, yet the process to design, implement, and evaluate these approaches is complex. Alternatives to policing responses are increasingly being implemented. However, evidence and implementation guidance for community-level public health approaches remain limited. This study protocol seeks to address community violence through a resilience framework-Adverse Community Experiences and Resilience (ACE|R)-being implemented in a major US city and leveraging a strategy of community organizing to advance community violence prevention. OBJECTIVE: The objective of this research is to understand the impact of community-level violence prevention interventions. Furthermore, we aim to describe the strategies of implementation and identify barriers to and facilitators of the approach. METHODS: This study uses a hybrid type 1 effectiveness-implementation design. Part 1 of the study will assess the effectiveness of the ACE|R framework plus community organizing by measuring impacts on violence- and health-related outcomes. To do so, we plan to collect quantitative data on homicides, fatal and nonfatal shootings, hospital visits due to nonaccidental injuries, calls for service, and other violence-related data. In Part 2 of the study, to assess the implementation of ACE|R plus community organizing, we will collect process data on community engagement events, deliver community trainings on community leadership and organizing, and conduct focus groups with key partners about violence and violence prevention programs in Milwaukee. RESULTS: This project received funding on September 1, 2020. Prospective study data collection began in the fall of 2021 and will continue through the end of 2023. Data analysis is currently underway, and the first results are expected to be submitted for publication in 2024. CONCLUSIONS: Community violence is a public health problem in need of community-centered solutions. Interventions that center community and leverage community organizing show promise in decreasing violence and increasing the well-being of community members. Methods to identify the impact of community-level interventions continue to evolve. Analysis of outcomes beyond violence-specific outcomes, including norms and community beliefs, may help better inform the short-term and proximal impacts of these community-driven approaches. Furthermore, hybrid implementation-effectiveness trials allow for the inevitable contextualization required to disseminate community interventions where communities drive the adaptations and decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50444.
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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 | 0,051 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,099 | 0,013 |
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 ».