Interest in the concept of moral injury among researchers, clinicians and policy makers can have undesirable consequences that are rarely considered. It can lead to misunderstanding of post-traumatic stress disorder, risks of primary and secondary gains for affected individuals and tertiary gains for third parties. This editorial calls for critical assessment of this sensitive matter.

Childhood adversities are major preventable risk factors for poor mental and physical health. Scientific advances in this area are not matched by clinical gains for affected individuals. We reflect on novel research directions that could accelerate clinical impact.

Psychiatrists have an essential role to play in promoting human rights in mental healthcare. The World Health Organization's QualityRights initiative, in partnership with different stakeholders, is improving the quality of psychiatric care in different countries.

Many mental disorders are linked to personality, but this is rarely recognised in clinical practice. It is suggested here that when the links are very close, the two can be joined. Galenic syndromes are so named because Galen was the first physician to recognise the links between personality and disease.

Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

A person's everyday language can indicate patterns of thought and emotion predictive of mental illness. Here, we discuss how natural language processing methods can be used to extract indicators of mental health from language to help address long-standing problems in psychiatry, along with the potential hazards of this new technology.

Cannabinoids are commonly perceived by the public as safe and effective for improving mental health, despite limited evidence to support their use. We discuss reasons why cannabinoids may be particularly compelling for our patients and provide strategies for how psychiatrists can counsel and educate patients on the evidence regarding cannabinoids.

Personalised prediction models promise to enhance the speed, accuracy and objectivity of clinical decision-making in psychiatry in the near future. This editorial elucidates key ethical issues at stake in the real-world implementation of prediction models and sets out practical recommendations to begin to address these.

There are many structural problems facing the UK at present, from a weakened National Health Service to deeply ingrained inequality. These challenges extend through society to clinical practice and have an impact on current mental health research, which was in a perilous state even before the coronavirus pandemic hit. In this editorial, a group of psychiatric researchers who currently sit on the Academic Faculty of the Royal College of Psychiatrists and represent the breadth of research in mental health from across the UK discuss the challenges faced in academic mental health research. They reflect on the need for additional investment in the specialty and ask whether this is a turning point for the future of mental health research.

Trieste mental health service is considered as one of the best mental healthcare systems in the world. This service is now under threat from right-wing politicians in the local region. We argue that this is a threat to progressive community psychiatry beyond Trieste and Italy. It is important for us to join forces with international colleagues and organisations in the campaign to defend and preserve the current service model in Trieste.

Despite extensive evidence for its effectiveness, electroconvulsive therapy remains the subject of fierce opposition from those contesting its benefits and claiming extreme harms. Alongside some reflections on my experiences of this treatment, I examine the case against electroconvulsive therapy and find that it appears to rest primarily on unsubstantiated claims about major ethical violations, rather than clinical factors such as effectiveness and risk.

Resilience is a dynamic, multi-level, multi-systemic process of positive adaptation at the individual, family and community levels. Promoting resilience can be a cost-effective form of preventive and early intervention, offering significant health advantages for young people throughout their lives. Developing resiliency interventions for youth and their families in low- and middle-income countries (LMICs), particularly in the context of the ongoing pandemic, is especially important given a lack of services and trained specialists, and poor levels of public spend on mental health, alongside marked and clustered psychosocial disadvantages and adverse childhood experiences. We propose a ‘hybrid’ model targeting 10- to 17 year-old children and their families, and options to engage through communities, schools and the family unit. These options will enhance individual and family resilience, and possibly buffer against adversity. The adaptations respect cultural and health beliefs, take account of structural drivers of inequalities and are suitable for LMICs.

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians’ heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and ‘big data’ approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

Electroconvulsive therapy is a highly effective treatment in psychiatry but still carries a stigma. One possible explanation is that it can be seen as a threat to personal autonomy.