Cross‐disciplinary partnerships between police and health services for mental health care
Notice bibliographique
Résumé
Police officers have increasingly become involved in mental healthcare responses not traditionally acknowledged as a police function. This has been described as "Florence Nightingale in pursuit of Willie Sutton" (a notorious bank robber) by American sociologist Egon Bittner (1974). In this editorial, we present emerging international approaches addressing this shift. Although these are often police and health collaborations, they have largely grown out of police services with health services, at times, hesitant partners. We argue that nurses must play a more active part in forging partnerships with law enforcement colleagues to provide innovative mental healthcare pathways. For the past 50 years, the literature reflects increasingly high rates of police interactions with people with mental health needs. One in 10 individuals encounters police in their pathway to mental health care (Livingston, 2016) with police being crucial gatekeepers to mental health services. Yet, officers report they feel ill-equipped and under-resourced to judge when, and what interventions are appropriate. This can result in untimely use of mental health legislation and arrests (Lancaster, 2016). Despite a rise of joint police/health responses to better support people in mental health crisis, law enforcement has been the driving force behind these initiatives. Yet, both agencies have very different priorities and perspectives on mental health care. Nurses therefore have a vital role in shaping the way in which these services work together. In 1988, a man with a history of mental illness and substance misuse was fatally shot by a Memphis police officer (Dupont & Cochran, 2000). This saw the first wave of police mental health interventionist models in the United States through police-led Crisis Intervention Teams (CIT's). CIT's seek to better support people with serious mental illness through specialist officer mental health training. This aims to minimize the use of force and to divert individuals from the criminal justice system towards mental health care. In Canada, Australia, the Netherlands and the United Kingdom, a second wave of proactive arrangements has developed. Coresponder models see mental health practitioners within police environments supporting officers. "Ride along" models see mental health practitioners in the field with officers, nurses support police in phone triage systems or colocate within police control rooms. These offer timely access to a range of more appropriate care pathways. In Sweden, health practitioners have taken the lead in improving community responses to psychiatric emergency calls traditionally handled by the police. PAM (Psychiatric Emergency Response Team) is a mobile unit staffed by two mental health nurses and a paramedic. Although still collaborating with police, these specialist crews can improve the timeliness to care and minimize the stigmatization of people with mental health problems by reducing the visibility of police in mental health emergencies. However, there is noteworthy disparity in the variety of international models which makes it difficult to collate data and compare evaluations. As such, there is limited long-term evidence of the impact on arrests, improvement of officer's attitudes or knowledge (Blevins, Lord, & Bjerregaard, 2014). Evaluations also vary in terms of how they improve occupational differences and potential tension between services and the perspectives of those they seek to support is sparse. Although there is still a need to fully establish an Evidence-Based Practice, the evaluations available do give an indication of the potential benefits and limitations interventions under these characteristics could provide. Yet, often police experiences with people with mental health needs sit out with serious crime, severe mental illness or mental health legal detention. The majority of contact is through more common mental health problems such as help-seeking requests from people with a diagnosis of personality disorder in mental health distress (Martin & Thomas, 2015). Often, these can be exacerbated by substance misuse, comorbidities or occur out-of-hours when primary health services are scant. This can drive police referral to emergency departments, essentially equipped to deal with time-critical medical emergencies. Moreover, local police are often absent from anticipatory care plans to support care. This is despite their frequent contact with people supported by both services. This results in lengthy wait times, preventable hospital admissions, professional tensions and increased resource demands on both services. A third wave of police/health collaboration recognizes opportunities of cooperation to enhance timely access to noncrisis resources. This has resulted in the emergence of police engaging with mental health early interventionist rather than reactive crisis services. This aims to find "upstream,"-targeted mental health referrals and reduce demand on existing crisis-led services. Cooperative approaches reflect a new police/health relationship with a growing appreciation of addressing the "root cause of police interactions with mental health" (Coleman & Cotton, 2012). An example of such innovation is a test of change in Baltimore, USA, which draws on evidence of heightened levels of mental health problems in high crime hotspots. Mental health specialists and police have merged data to identify, engage and direct services to people disconnected from health care (White & Weisburd,2017). Positively, through "generations" of police/mental health collaborations, there is increasing recognition of shared common ground. Police/health partnerships are emerging from something that exists on the edges of traditional practice, into core health/police business (Van Dijk & Crofts, 2017). Despite these promising developments, Wood and Beierschmitt (2014) suggest there remains a "grey zone" of care within routine policing as a result of people being unengaged, disengaged or under-supported by mental health services. We argue a further explanation. There are fundamental differences in how both professions view and value each other's roles, priorities and the needs of people in mental health distress. If such collaborations are to be successful, we need to understand these perspectives. There are immense opportunities to bolster these understandings by bringing a strong mental health nursing voice to police/health cross-disciplinary education, practice and research. Such unions are complex, yet we need to recognize the important contribution we can make with law enforcement colleagues to practice innovations, policy development, joint learning and knowledge coproduction. We join in the call for mental health practitioners to be active with police colleagues (Watson and Fulambarker, 2012) and those with lived/living experience of the police/mental health intersect. Only then can we shape the landscape of contemporary partnerships.
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 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 | 0,008 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,006 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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 ».