What experts say about domestic violence: constructing Thailand’s domestic violence severity index through an expert judgement approach
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Purpose While many countries have adopted traditional approaches to identify patterns and trends in domestic violence at a national level, a strategy that provides more insightful information is still lacking. In response to this need, the purpose of this study is to propose the construction of a domestic violence severity index (DVSI) as an alternative. This index serves as a strategic instrument for policymakers and law enforcement agencies, enabling them to monitor changes in the overall severity of domestic violence incidents over time, beyond relying solely on the volume of reported incidents. Design/methodology/approach Reported domestic violence incidents are collected over the past five years (2019–2023) from the entire country. Unlike sentence-based approaches such as the Cambridge Crime Harm Index and the Canadian Crime Severity Index, the DVSI applies a crime severity index based on expert judgment to assess the seriousness of domestic violence categories. Twenty-three experts with extensive experience in domestic violence issues across various governmental and nongovernmental organizations participated in providing assessments. To ensure consistency in assigning weight values to the domestic violence categories, the average scores provided by experts were calculated using arithmetic mean, median, mode and geometric mean. Findings Domestic violence maps reflecting trends between 2019 and 2023 across 77 provinces in Thailand have been generated based on the index data. The maps depict significant serial and spatial correlation levels from 2019 to 2023. Practical implications These maps carry significant implications for the country’s domestic violence prevention strategy by offering detailed insights into the geographical locations and periods requiring focused attention and resource allocation from the government. This tool can also aid the public in gaining a better understanding of the prevalence of domestic violence in society, facilitating increased coordination and collaboration among stakeholders. Originality/value Many countries quantify domestic violence using simple methods, such as calculating percentages or measuring incidents per 100,000 population. However, a specific DVSI has not yet been developed to analyze and understand domestic violence trends geographically, which could serve as an additional measure to protect victims. In addition, the study uses an expert judgment approach, a rare method in constructing a crime severity index, especially in the context of domestic violence.
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.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle