In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise.

Background

Background
The COVID-19 pandemic has impacted on psychiatric symptoms of children and young people, but many psychiatric services have been disrupted. It is unclear how service use, self-harm and suicide has changed since the pandemic started. To gain timely information, this systematic review focused on studies based on administrative data that compared psychiatric service use, self-harm and suicide before and during the pandemic among children and young people.

This report warns that, because of the conflict in Ukraine, millions of children are likely to suffer from mental health impacts now and into the future. World Vision is concerned that the war is subjecting children to constant fear and hopelessness, increasing their immediate stress responses and increasing their risk for specific mental disorders such as PTSD, depression, and anxiety.