Project description:ObjectiveCommunity Partners in Care (CPIC) was a group-randomized study of two approaches to implementing expanded collaborative depression care: Community Engagement and Planning (CEP), a coalition approach, and Resources for Services (RS), a technical assistance approach. Collaborative care networks in both arms involved health care and other agencies in five service sectors. This study examined six- and 12-month outcomes for CPIC participants with serious mental illness.MethodsThis secondary analysis focused on low-income CPIC participants from racial-ethnic minority groups with serious mental illness in underresourced Los Angeles communities (N=504). Serious mental illness was defined as self-reported severe depression (≥20 on the Patient Health Questionnaire-8) at baseline or a lifetime history of bipolar disorder or psychosis. Logistic and Poisson regression with multiple imputation and response weights, controlling for covariates, was used to model intervention effects.ResultsAmong CPIC participants, 50% had serious mental illness. Among those with serious mental illness, CEP relative to RS reduced the likelihood of poor mental health-related quality of life (OR=.62, 95% CI=.41-.95) but not depression (primary outcomes); reduced the likelihood of having homelessness risk factors and behavioral health hospitalizations; increased the likelihood of mental wellness; reduced specialty mental health medication and counseling visits; and increased faith-based depression visits (each p<.05) at six months. There were no statistically significant 12-month effects.ConclusionsFindings suggest that a coalition approach to implementing expanded collaborative depression care, compared with technical assistance to individual programs, may reduce short-term behavioral health hospitalizations and improve mental health-related quality of life and some social outcomes for adults with serious mental illness, although no evidence was found for long-term effects in this subsample.

Project description:BACKGROUND:Mental health services aim to provide recovery-focused care and facilitate coproduced care planning. In practice, mental health providers can find supporting individualized coproduced care with service users difficult while balancing administrative and performance demands. To help meet this aim and using principles of coproduction, an innovative mobile digital care pathway tool (CPT) was developed to be used on a tablet computer and piloted in the West of England. OBJECTIVE:The aim of this study was to examine mental health care providers' views of and experiences with the CPT during the pilot implementation phase and identify factors influencing its implementation. METHODS:A total of 20 in-depth telephone interviews were conducted with providers participating in the pilot and managers in the host organization. Interviews were audio recorded, transcribed, anonymized, and thematically analyzed guided by the Consolidated Framework for Implementation Research. RESULTS:The tool was thought to facilitate coproduced recovery-focused care planning, a policy and organizational as well as professional priority. Internet connectivity issues, system interoperability, and access to service users' health records affected use of the tool during mobile working. The organization's resources, such as information technology (IT) infrastructure and staff time and IT culture, influenced implementation. Participants' levels of use of the tool were dependent on knowledge of the tool and self-efficacy; perceived service-user needs and characteristics; and perceptions of impact on the therapeutic relationship. Training and preparation time influenced participants' confidence in using the tool. CONCLUSIONS:Findings highlight the importance of congruence between staff, organization, and external policy priorities and digital technologies in aiding intervention engagement, and the need for ongoing training and support of those intended to use the technology during and after the end of implementation interventions.

Project description:BackgroundPeer support has been suggested as an alternative or complement to professional support for mothers with perinatal mental health difficulties. The aim of this realist review was to synthesise the evidence on perinatal mental health peer support programmes outside mental health services, to understand what is it about community-based perinatal mental health peer support that works, for whom, in what circumstances, in what respects, and why.MethodsApplying realist methodology, an initial theoretical model was tested against evidence from empirical studies. 29 empirical studies were included, covering 22 antenatal and postnatal mental health interventions that offered one-to-one or group peer support, in person or by telephone. Data extraction identified the configurations of contexts (C), mechanisms (M) and outcomes (O) relevant to mothers' use of peer support and to the positive and negative effects of using peer support.Results13 C-M-O configurations explained take-up of peer support. These were based on mothers' perceptions that peer support would offer empathetic understanding and non-judgemental acceptance outside their social circle; their relationships with primary health professionals; their cultural background and perspectives on mental health; their desire for professional support; overcoming practical barriers; the format of the support; and the use of volunteers. A further 13 C-M-O configurations explained positive impact on mothers. These were based on receiving empathetic listening, acceptance, affirmation and normalisation; peers sharing ideas about self-care, coping, and services; peers using therapeutic techniques; the opportunity to give support to others; meaningful social relationships with volunteers and other mothers; and other benefits of attending a group. There were 8 C-M-O configurations explaining negative impact. These were based on lack of validation; self-criticism from downward and upward social comparison; a culture of negativity; peers being judgemental or directive; not feeling heard; peer support as a stressful social relationship; and distress at endings.ConclusionsPeer support works in complex ways that are affected by personal and social contexts. Providers, commissioners and evaluators can use this review to understand and maximise the valuable benefits of peer support, to minimise potential risks, and to devise ways of reaching mothers who do not currently engage with it.

Project description:BackgroundPeer support for people with long-term mental health problems is central to recovery-oriented approaches in mental health care. Peer support has traditionally been conducted offline in face-to-face groups, while online groups on the Internet have increased rapidly. Offline and online peer support groups are shown to have differing strengths and weaknesses. However, little is known about how combining the two formats might be experienced by service users, which this paper aims to illuminate.MethodsIn this exploratory and descriptive study, a recovery-oriented Internet-based portal called ReConnect was used by service users in two mental health communities in Norway for 6-12 months. The portal included an online peer support group which also facilitated participation in local offline peer support groups. Both group formats were moderated by an employed service user consultant. Qualitative data about service users' experiences were collected through focus groups and individual interviews and inductively analyzed thematically.ResultsA total of 14 female service users 22-67 years of age with various diagnoses participated in three focus groups and 10 individual interviews. Two main themes were identified: (1) balancing anonymity and openness, and (2) enabling connectedness. These themes are further illustrated with the subthemes: (i) dilemmas of anonymity and confidentiality, (ii) towards self-disclosure and openness, (iii) new friendships, and (iv) networks in the local community. Three of the subthemes mainly describe benefits, while challenges were more implicit and cut across the subthemes. Identified challenges were related to transitions from anonymity online to revealing one's identity offline, confidentiality, and barriers related to participation in offline peer support groups.ConclusionsThis study suggests that online and offline peer support groups complement each other, and that combining them is mainly described as beneficial by service users. Identified benefits appeared to arise from service users' options of one format or the other, or that they could combine formats in ways that suited their individual values and comfort zones. Moderation by a trained service user consultant appeared essential for both formats and can be used systematically to address identified challenges. Combining online and offline peer support groups is a promising concept for facilitating recovery-oriented care and warrants continued research.

Project description:BackgroundArtificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.ObjectiveThis study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs).MethodsThis study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted.ResultsThe final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback.ConclusionsCommercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Project description:BackgroundGiven the scarcity of specialist mental healthcare in India, diverse community mental healthcare models have evolved. This study explores and compares Indian models of mental healthcare delivered by primary-level workers (PHW), and health workers' roles within these. We aim to describe current service delivery to identify feasible and acceptable models with potential for scaling up.MethodsSeventy two programmes (governmental and non-governmental) across 12 states were visited. 246 PHWs, coordinators, leaders, specialists and other staff were interviewed to understand the programme structure, the model of mental health delivery and health workers' roles. Data were analysed using framework analysis.ResultsProgrammes were categorised using an existing framework of collaborative and non-collaborative models of primary mental healthcare. A new model was identified: the specialist community model, whereby PHWs are trained within specialist programmes to provide community support and treatment for those with severe mental disorders. Most collaborative and specialist community models used lay health workers rather than doctors. Both these models used care managers. PHWs and care managers received support often through multiple specialist and non-specialist organisations from voluntary and government sectors. Many projects still use a simple yet ineffective model of training without supervision (training and identification/referral models).Discussion and conclusionIndian models differ significantly to those in high-income countries-there are less professional PHWs used across all models. There is also intensive specialist involvement particularly in the community outreach and collaborative care models. Excessive reliance on specialists inhibits their scalability, though they may be useful in targeted interventions for severe mental disorders. We propose a revised framework of models based on our findings. The current priorities are to evaluate the comparative effectiveness, cost-effectiveness and scalability of these models in resource-limited settings both in India and in other low- and middle- income countries.

Project description:BackgroundSupporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time.MethodsWe included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit.ResultsTwenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data.ConclusionsNOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic.

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Community models of mental care warrant more governmental support.

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Community models of mental care warrant more governmental support.

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