Development of the interRAI Brief Mental Health Screener to Enhance the Ability of Police Officers to Identify Persons with Serious Mental Disorder
Notice bibliographique
Résumé
Background: Police officers are often the first to respond when persons experience a mental health crisis in the community. They must de-escalate volatile situations involving persons with serious mental disorder (PSMD) and bring the person to the attention of either the criminal justice or mental health care system. It is argued that issues such as repeated police contact, excessive emergency department (ED) wait times, and the criminalization of the mentally ill are evidence that the current system lacks the ability to meet the needs of PSMD. Critics have argued the source of the problem is inadequate police training, and insufficient and poorly organized community mental health services. Others claim that the underlying issue is that the current system for responding to PSMD is dysfunctional. The model is based on the concept that the best way to meet the needs of PSMD is through the integration of systems and services which to date, has remained an impossible goal. Given the current system will not be replaced anytime soon, efforts should be directed toward developing innovative ways to make it easier for the systems to work more effectively together. \nObjectives: The major objective of this dissertation was to develop and pilot a new mental health screening form, the interRAI Brief Mental Health Screener (BMHS)* to enhance the ability of police officers to identify PSMD, and to support their decision-making. A second objective was to develop a model that best predicts which persons are most likely to be taken to hospital by police officers and which persons most likely to be admitted. A final objective was to analyze the impact that interacting with PSMD has on police resources in terms of the amount of time police officers spend on mental health related calls for service. \nMethodology: Logistic regression analysis was used to identify 14 predictors of serious mental health disorders from 41,019 cases obtained from the main Resident Assessment Instrument for \nMental Health (RAI-MH) database. The RAI-MH is a comprehensive mental health assessment system that is currently used for all persons admitted into a psychiatric hospital in Ontario. Additional clinical, demographic and contextual items were added after consultation with an advisory committee composed of representatives from hospitals and police services resulting in a pilot version of the interRAI BMHS. The County of Wellington and the city of Guelph were selected as the setting for the pilot that included 4 general hospitals, 1 psychiatric facility and the participation of the Ontario Provincial Police (OPP) and the Guelph Police Service. After training police officers to use the new form, the interRAI BMHS was pilot tested over a seven month period commencing May 2011. Hospital records were also accessed to determine patient disposition. Logistic regression was used to develop an algorithm to identify the persons with the highest probability of being taken to hospital by police officers, and those persons who were most likely to be admitted. \nResults: Police officers from the two jurisdictions in Ontario completed a total of 235 interRAI BMHS forms. Chi square analysis revealed the most common reasons why police officers take persons to hospital included the person considering performing a self-injurious act in the past 30 days, and family, and others were concerned the person was at risk for self-injury. Intoxication by drugs or alcohol and having symptoms of psychosis were not significant reasons for police officers to take a person to hospital. The variables most associated with being admitted after being taken to hospital, included indicators of disordered, such as lack of insight into their mental health problems, abnormal thought process, delusions and hallucinations. Overall, although the terminology differed, the same patterns emerged in the pilot study that previous research reported. Police officers tend to focus on dangerousness and public safety, while clinicians are concerned with indicators of disordered thought. Logistic regression analysis \nrevealed that the 14 variable algorithm used to construct the interRAI BMHS was a good predictor of who was most likely to be taken to hospital by police officers, and who was most likely to be admitted. Another important finding was that the reasons why police officers take persons to hospital were not the same as the reasons why persons are subsequently admitted. This suggests the criminal justice, health and mental health systems are not synchronized. The research also revealed that police officers spend a mean time of over three hours overall devoted to calls for service involving PSMD, and a mean time of just over three hours waiting in the ED. \nConclusion: The interRAI BMHS provides useful information for both police officers and ED staff regarding the variables significantly associated with serious mental disorder. It will help support police officer and ED decision-making, and it will contribute to enhancing the training provided to police officers and mental health service providers. Additional research and larger sample sizes will help to further refine the instrument. The interRAI BMHS is based on health system data and written in the language of the health system. As such, it has the potential to both enhance the ability of police officers and other mental health service providers to identify indicators of serious mental disorder, and to help synchronize the criminal justice and mental health care systems. \n*interRAI stands for the international resident assessment instrument, in international collaborative to improve the quality of life of vulverable persons through a seamless comprehensive assessment system.
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 | 0,000 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».