A Novel Training Program for Police Officers that Improves Interactions with Mentally Ill Individuals and is Cost-Effective
Notice bibliographique
Résumé
Police and law enforcement providers frequently come into contact with individuals who have psychiatric disorders, sometimes with tragic results. Repeated studies suggest that greater understanding of psychiatric conditions by police officers would be beneficial. Here we present a novel approach to training police officers to improve their interactions with those who might have a mental illness. This approach involved developing a carefully scripted role-play training, which involved police officers (n = 663) interacting with highly trained actors during six realistic scenarios. The primary goal of the training was to improve empathy, communication skills, and the ability of officers to de-escalate potentially difficult situations. Uniquely, feedback was given to officers after each scenario by several individuals including experienced police officers, a mental health professional, and by the actors involved (with insights such as "this is how you made me feel"). Results showed that there were no changes in attitudes of the police toward the mentally ill comparing data at baseline and at 6 months after the training in those who completed both ratings (n = 170). In contrast, there were significant improvements in directly measured behaviors (n = 142) as well as in indirect measurements of behavior throughout the police force. Thus, compared to previous years, there was a significant increase in the recognition of mental health issues as a reason for a call (40%), improved efficiency in dealing with mental health issues, and a decrease in weapon or physical interactions with mentally ill individuals. The training cost was $120 per officer but led to significant cost savings (more than $80,000) in the following 6 months. In conclusion, this novel 1-day training course significantly changed behavior of police officers in meaningful ways and also led to cost savings. We propose that this training model could be adopted by other police agencies.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 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 ».