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Record W2803524916 · doi:10.1111/jpm.12471

Cross‐disciplinary partnerships between police and health services for mental health care

2018· editorial· en· W2803524916 on OpenAlexaboutno aff
Inga Heyman, Emma McGeough

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2018
Typeeditorial
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthDisciplineHealth careNursingMEDLINEMedicinePsychologyEnvironmental healthPsychiatryPolitical science

Abstract

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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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.007
Scholarly communication0.0100.010
Open science0.0020.009
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0110.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.448
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations13
Published2018
Admission routes1
Has abstractyes

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