Exploring Mental Health Professionals’ Experiences of Intimate Partner Violence–Related Training: Results From a Global Survey
Notice bibliographique
Résumé
Intimate partner violence (IPV) is a global public health problem that has been shown to lead to serious mental health consequences. Due to its frequent co-occurrence with psychiatric disorders, it is important to assess for IPV in mental health settings to improve treatment planning and referral. However, lack of training in how to identify and respond to IPV has been identified as a barrier for the assessment of IPV. The present study seeks to better understand this IPV-related training gap by assessing global mental health professionals’ experiences of IPV-related training and factors that contribute to their likelihood of receiving training. Participants were French-, Spanish-, and Japanese-speaking psychologists and psychiatrists ( N = 321) from 24 nations differing on variables related to IPV, including IPV prevalence, IPV-related norms, and IPV-related laws. Participants responded to an online survey asking them to describe their experiences of IPV-related training (i.e., components and hours of training) and were asked to rate the frequency with which they encountered IPV in clinical practice and their level of knowledge and experience related to relationship problems; 53.1% of participants indicated that they had received IPV-related training. Clinicians from countries with relatively better implemented laws addressing IPV and those who encountered IPV more often in their regular practice were more likely to have received training. Participants who had received IPV-related training, relative to those without training, were more likely to report greater knowledge and experience related to relationship problems. Findings suggest that clinicians’ awareness of IPV and the institutional context in which they practice are related to training. Training, in turn, is associated with subjective appraisals of knowledge and experience related to relationship problems. Increasing institutional efforts to address IPV (e.g., implementing IPV legislation) may contribute to improved practices with regard to IPV in mental health settings.
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 | 0,003 | 0,001 |
| 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 | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».