SUMMARY: Trauma-informed approaches emerged partly in response to research demonstrating that trauma is widespread across society, that it is highly correlated with mental health and that this is a costly public health issue. The fundamental shift in providing support using a trauma-informed approach is to move from thinking ‘What is wrong with you?’ to considering ‘What happened to you?’. This article, authored by trauma survivors and service providers, describes trauma-informed approaches to mental healthcare, why they are needed and how barriers can be overcome so that they can be implemented as an organisational change process. It also describes how past trauma can be understood as the cause of mental distress for many service users, how service users can be retraumatised by ‘trauma-uninformed’ staff and how staff can experience vicariously the service user's trauma and can themselves be traumatised by practices such as restraint and seclusion. Trauma-informed mental healthcare offers opportunities to improve service users' experiences, improve working environments for staff, increase job satisfaction and reduce stress levels by improving the relationships between staff and patients through greater understanding, respect and trust.LEARNING OBJECTIVES: •Appreciate broad-based definitions of trauma •Gain an understanding of what trauma-informed approaches are and why they have emerged, including the potential for (re)traumatisation in the mental health system •Consider how to practise trauma-informed approaches, including in ‘trauma-uninformed’ organisations, and the potential barriers to and opportunities from doing so DECLARATION OF INTEREST: A. S. is funded by a National Institute for Health Research (NIHR) Post-Doctoral Fellowship. This article presents independent research partially funded by the NIHR. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health.

Purpose: The purpose of this paper is to describe and explain trauma-informed approaches (TIAs) to mental health. It outlines evidence on the link between trauma and mental health, explains the principles of TIAs and their application in mental health and explores the extent to which TIAs are impacting in the UK. Design/methodology/approach: The approach is a conceptual account of TIAs including a consideration of why they are important, what they are and how they can become more prevalent in the UK. This is supported by a narrative overview of literature on effectiveness and a scoping of the spread of TIAs in the UK. Findings: There is strong and growing evidence of a link between trauma and mental health, as well as evidence that the current mental health system can retraumatise trauma survivors. There is also emerging evidence that trauma-informed systems are effective and can benefit staff and trauma survivors. Whilst TIAs are spreading beyond the USA where they developed, they have made little impact in the UK. The reasons for this are explored and ways of overcoming barriers to implementation discussed. Originality/value: This paper – authored by trauma survivors and staff – describes an innovative approach to mental health service provision that, it is argued, could have immense benefits for staff and service users alike.

(2018). (Mis)understanding trauma-informed approaches in mental health. Journal of Mental Health. Ahead of Print.

Background: Health research is frequently conducted in multi‐disciplinary teams, with these teams increasingly including service user researchers. Whilst it is common for service user researchers to be involved in data collection – most typically interviewing other service users – it is less common for service user researchers to be involved in data analysis and interpretation. This means that a unique and significant perspective on the data is absent.Aim: This study aims to use an empirical report of a study on Cognitive Behavioural Therapy for psychosis (CBTp) to demonstrate the value of multiple coding in enabling service users voices to be heard in team‐based qualitative data analysis.Design: The CBTp study employed multiple coding to analyse service users’ discussions of CBT for psychosis (CBTp) from the perspectives of a service user researcher, clinical researcher and psychology assistant. Multiple coding was selected to enable multiple perspectives to analyse and interpret data, to understand and explore differences and to build multi‐disciplinary consensus.Results: Multiple coding enabled the team to understand where our views were commensurate and incommensurate and to discuss and debate differences. Through the process of multiple coding, we were able to build strong consensus about the data from multiple perspectives, including that of the service user researcher.Discussion: Multiple coding is an important method for understanding and exploring multiple perspectives on data and building team consensus. This can be contrasted with inter‐rater reliability which is only appropriate in limited circumstances.Conclusion: We conclude that multiple coding is an appropriate and important means of hearing service users’ voices in qualitative data analysis.

Outcome measures for cognitive behavior therapy for psychosis (CBTp) have been derived from pharmacological studies, focusing on symptom change rather than outcomes such as distress or fulfilment. This study presents the development and psychometric properties of a new outcome measure (CHoice of Outcome In Cbt for psychosEs [CHOICE]), which reflects more strongly the aims of CBTp and the priorities of service users. Service users who had received CBTp participated in focus groups to discuss their outcome priorities, using a topic guide generated by a panel of experts in CBTp. A qualitative thematic analysis was undertaken to reach consensus on themes and generate items. Response scales were constructed for 3 dimensions: severity, satisfaction, and importance. The resulting questionnaire was piloted with service users who had not received CBTp, stratified by service type, ethnicity, and first language to ensure that it was user friendly and applicable prior to CBTp. The psychometric properties of the measure were then examined in a sample of 152 service users. Twenty-four items, and 2 of the dimensions (severity and satisfaction), were retained in the final measure. A factor analysis revealed a single psychological recovery factor interspersed throughout with both CBTp and recovery items. Test-retest reliability, construct validity, and sensitivity to change following CBTp were confirmed. The CHOICE measure is unique in being the first psychometrically adequate service user–led outcome measure of CBTp. It provides the opportunity to examine the evidence base for CBTp with an assessment approach that prioritizes service user definitions of recovery and CBT aims.

It is becoming increasingly recognized that conventionally derived outcome measures in mental health research are problematic. This is both because of the methodology used and because a ‘good’ outcome is framed from the perspective of clinicians and researchers. This paper describes a methodology for developing outcome measures for use in large studies entirely from the perspective of mental health service users. It is a mixed methods model starting with a participatory and qualitative methodology and proceeding to psychometric testing. At all stages, the researchers are themselves mental health service users. In the first phase of the model, focus groups are convened comprising people who have received the treatment or service being measured. The focus groups meet twice resulting in a draft mixed-methods questionnaire devised from thematic analysis of the focus group data. This is then taken to expert panels, again comprising individuals who have received the treatment or service being evaluated for refinement. Following this, a feasibility study is conducted with N ∼ 50 participants and changes made iteratively to the questionnaire in light of feedback. The final measure is subject to psychometric testing both to ensure it is robust and to explore similarities and differences with conventionally derived measures.