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Aims: To assess trajectories of women’s experience of intimate partner violence (IPV) over time, and baseline risk factors and associated four-year outcomes for different trajectories. Design: A cohort study of 548 women with depressive symptoms, attending primary care appointments, were surveyed annually for four years. Secondary analysis was undertaken using growth mixture modelling to generate IPV trajectories. Analyses of associations of these generated classes of IPV with hypothesised baseline and four-year measures were undertaken. Results: At baseline, 42% (231) women experienced IPV in past 12 months. Five-class IPV trajectory model showed five groups over time: consistently ‘high IPV’ (5%, n=28), ‘some IPV’ (14%, n=77), ‘minimal IPV’ (9%, n=52), ‘decreasing IPV’ (11%, n=62), and ‘no IPV’ (60%, n=329). Baseline differences showed women in ‘high’ and ‘some’ group had more childhood abuse, low income and poor mental health compared to ‘minimal’ or ‘no IPV’ groups. At four years, ‘decreasing IPV’ group was aligned with ‘minimal/no IPV’ groups on mental health, quality of life and social support measures. Conclusion: Women exhibited different trajectories of IPV over time with high burden of mental health problems, except for when IPV decreases. Clinical identification of IPV and tailoring of responses to decrease exposure to IPV is warranted to reduce disease burden.

OBJECTIVE: To evaluate a facilitated, 90-min session, delivered for four weeks, Online Carer Wellbeing and Connection Program in Victoria, Australia. METHODS: One hundred and three carers took part in the evaluation. Eighty-six completed both pre- and post-program surveys evaluating program impacts on psychological distress, perceived loneliness, and social support. Qualitative interviews were conducted (n = 76) post-program for experiential data. FINDINGS: Paired samples t-tests showed significant decreases between pre- and post-program for psychological distress (M = 25.10, SD = 7.08; M = 22.00, SD = 6.57; t(85) = 4.88, p = 0.000), perceived loneliness (M = 6.69, SD = 1.89; M = 6.14, SD = 1.76; t(85) = 3.45, p = 0.000) and perceived social support (M = 8.31, SD = 2.48; M = 8.83, SD = 2.21; t(85) = -2.54, p = 0.013). Thematic analysis identified positive experiences and the mechanisms of action (or the ingredients for program success) as: 1. Delivery by a trained facilitator; 2. Provision of respite for person being cared for during meetings; 3. Technical assistance; 4. Online modality; 5. Inclusivity; 6. Diversity of experience; 7. Shared understanding; 8. Safety; 9. Emotional release; 10. Reflection, and; 11. Self-care practices. INNOVATION: A model illustrating the mechanisms of action based on the findings of the mixed-methods evaluation is presented to support wider implementation and translation. CONCLUSION: The online program effectively reduced psychological distress and loneliness and improved carer wellbeing.

There is increased recognition that people with lived-experience of mental ill-health ought to be centred in research design, implementation and translation, and quality improvement and program evaluation of services. There is also an increased focus on ways to ensure that co-design processes can be led by people with lived-experience of mental ill-health. Despite this, there remains limited explanation of the physical, social, human, and economic infrastructure needed to create and sustain such models in research and service settings. This is particularly pertinent for all health service sectors (across mental and physical health and social services) but more so across tertiary education settings where research generation occurs for implementation and translation activities with policy and services. The Co-Design Living Labs program was established in 2017 as an example of a community-based embedded approach to bring people living with trauma and mental ill-health and carers/family and kinship group members together with university-based researchers to drive end-to-end research design to translation in mental healthcare and research sectors. The program's current membership is near to 2000 people. This study traces the evolution of the program in the context of the living labs tradition of open innovation. It overviews the philosophy of practice for working with people with lived-experience and carer/family and kinship group members-togetherness by design. Togetherness by design centres on an ethical relation of being-for that moves beyond unethical and transactional approaches of being-aside and being-with, as articulated by sociologist Zygmunt Bauman. The retrospective outlines how an initial researcher-driven model can evolve and transform to become one where people with lived-experience of mental ill-health and carer/family kinship group members hold clear decision-making roles, share in power to enact change, and move into co-researcher roles within research t

BACKGROUND: The volume and heterogeneity of mental health problems that primary care patients present with is a substantial challenge for health systems, and both undertreatment and overtreatment are common. We developed Link-me, a patient-completed Decision Support Tool, to predict severity of depression or anxiety, identify priorities, and recommend interventions. In this study, we aimed to examine if Link-me reduces psychological distress among individuals predicted to have minimal/mild or severe symptoms of anxiety or depression. METHODS: In this pragmatic stratified randomised controlled trial, adults aged 18-75 years reporting depressive or anxiety symptoms or use of mental health medication were recruited from 23 general practices in Australia. Participants completed the Decision Support Tool and were classified into three prognostic groups (minimal/mild, moderate, severe), and those in the minimal/mild and severe groups were eligible for inclusion. Participants were individually and randomly assigned (1:1) by a computer-generated allocation sequence to receive either prognosis-matched care (intervention group) or usual care plus attention control (control group). Participants were not blinded but intervention providers were only notified of those allocated to the intervention group. Outcome assessment was blinded. The primary outcome was the difference in the change in scores between the intervention and control group, and within prognostic groups, on the 10-item Kessler Psychological Distress Scale at 6 months post randomisation. The trial was registered on the Australian and New Zealand Clinical Trials Registry, ACTRN12617001333303. OUTCOMES: Between Nov 21, 2017, and Oct 31, 2018, 24 616 patients were invited to complete the eligibility screening survey. 1671 of these patients were included and randomly assigned to either the intervention group (n=834) or the control group (n=837). Prognosis-matched care was associated with greater reductions in psycholog

BACKGROUND: Mental health treatment rates are increasing, but the burden of disease has not reduced. Tools to support efficient resource distribution are required. AIM: To investigate whether a person-centred e-health (Target-D) platform matching depression care to symptom severity prognosis can improve depressive symptoms relative to usual care. DESIGN AND SETTING: Stratified individually randomised controlled trial in 14 general practices in Melbourne, Australia, from April 2016 to February 2019. In total, 1868 participants aged 18-65 years who had current depressive symptoms; internet access; no recent change to antidepressant; no current antipsychotic medication; and no current psychological therapy were randomised (1:1) via computer-generated allocation to intervention or usual care. METHOD: The intervention was an e-health platform accessed in the GP waiting room, comprising symptom feedback, priority-setting, and prognosis-matched management options (online self-help, online guided psychological therapy, or nurse-led collaborative care). Management options were flexible, neither participants nor staff were blinded, and there were no substantive protocol deviations. The primary outcome was depressive symptom severity (9-item Patient Health Questionnaire [PHQ-9]) at 3 months. RESULTS: In intention to treat analysis, estimated between- arm difference in mean PHQ-9 scores at 3 months was -0.88 (95% confidence interval [CI] = -1.45 to -0.31) favouring the intervention, and -0.59 at 12 months (95% CI = -1.18 to 0.01); standardised effect sizes of -0.16 (95% CI = -0.26 to -0.05) and -0.10 (95% CI = -0.21 to 0.002), respectively. No serious adverse events were reported. CONCLUSION: Matching management to prognosis using a person-centred e-health platform improves depressive symptoms at 3 months compared to usual care and could feasibly be implemented at scale. Scope exists to enhance the uptake of management options.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of health care workers' mental health and well-being for the successful function of the health care system. Few targeted digital tools exist to support the mental health of hospital-based health care workers, and none of them appear to have been led and co-designed by health care workers. OBJECTIVE: RMHive is being led and developed by health care workers using experience-based co-design (EBCD) processes as a mobile app to support the mental health challenges posed by the COVID-19 pandemic to health care workers. We present a protocol for the impact evaluation for the rapid design and delivery of the RMHive mobile app. METHODS: The impact evaluation will adopt a mixed methods design. Qualitative data from photo interviews undertaken with up to 30 health care workers and semistructured interviews conducted with up to 30 governance stakeholders will be integrated with qualitative and quantitative user analytics data and user-generated demographic and mental health data entered into the app. Analyses will address three evaluation questions related to engagement with the mobile app, implementation and integration of the app, and the impact of the app on individual mental health outcomes. The design and development will be described using the Mobile Health Evidence Reporting and Assessment guidelines. Implementation of the app will be evaluated using normalization process theory to analyze qualitative data from interviews combined with text and video analysis from the semistructured interviews. Mental health impacts will be assessed using the total score of the 4-item Patient Health Questionnaire (PHQ4) and subscale scores for the 2-item Patient Health Questionnaire for depression and the 2-item Generalized Anxiety Scale for anxiety. The PHQ4 will be completed at baseline and at 14 and 28 days. RESULTS: The anticipated average use period of the app is 30 days. The rapid design will occur over four months us

BACKGROUND: Mental health policies outline the need for codesign of services and quality improvement in partnership with service users and staff (and sometimes carers), and yet, evidence of systematic implementation and the impacts on healthcare outcomes is limited. OBJECTIVE: The aim of this study was to test whether an adapted mental health experience codesign intervention to improve recovery-orientation of services led to greater psychosocial recovery outcomes for service users. DESIGN: A stepped wedge cluster randomized-controlled trial was conducted. SETTING AND PARTICIPANTS: Four Mental Health Community Support Services providers, 287 people living with severe mental illnesses, 61 carers and 120 staff were recruited across Victoria, Australia. MAIN OUTCOME MEASURES: The 24-item Revised Recovery Assessment Scale (RAS-R) measured individual psychosocial recovery. RESULTS: A total of 841 observations were completed with 287 service users. The intention-to-treat analysis found RAS-R scores to be similar between the intervention (mean = 84.7, SD= 15.6) and control (mean = 86.5, SD= 15.3) phases; the adjusted estimated difference in the mean RAS-R score was -1.70 (95% confidence interval: -3.81 to 0.40; p = .11). DISCUSSION: This first trial of an adapted mental health experience codesign intervention for psychosocial recovery outcomes found no difference between the intervention and control arms. CONCLUSIONS: More attention to the conditions that are required for eight essential mechanisms of change to support codesign processes and implementation is needed. PATIENT AND PUBLIC INVOLVEMENT: The State consumer (Victorian Mental Illness Awareness Council) and carer peak bodies (Tandem representing mental health carers) codeveloped the intervention. The adapted intervention was facilitated by coinvestigators with lived-experiences who were coauthors for the trial and process evaluation protocols, the engagement model and explanatory model of change for the trial.

The COVID-19 pandemic has highlighted the importance of health care workers’ mental health and well-being for the successful function of the health care system. Few targeted digital tools exist to support the mental health of hospital-based health care workers, and none of them appear to have been led and co-designed by health care workers. ec> RMHive is being led and developed by health care workers using experience-based co-design (EBCD) processes as a mobile app to support the mental health challenges posed by the COVID-19 pandemic to health care workers. We present a protocol for the impact evaluation for the rapid design and delivery of the RMHive mobile app. ec> The impact evaluation will adopt a mixed methods design. Qualitative data from photo interviews undertaken with up to 30 health care workers and semistructured interviews conducted with up to 30 governance stakeholders will be integrated with qualitative and quantitative user analytics data and user-generated demographic and mental health data entered into the app. Analyses will address three evaluation questions related to engagement with the mobile app, implementation and integration of the app, and the impact of the app on individual mental health outcomes. The design and development will be described using the Mobile Health Evidence Reporting and Assessment guidelines. Implementation of the app will be evaluated using normalization process theory to analyze qualitative data from interviews combined with text and video analysis from the semistructured interviews. Mental health impacts will be assessed using the total score of the 4-item Patient Health Questionnaire (PHQ4) and subscale scores for the 2-item Patient Health Questionnaire for depression and the 2-item Generalized Anxiety Scale for anxiety. The PHQ4 will be completed at baseline and at 14 and 28 days. ec>