Abstract: To mark the 20th anniversary of the lifting of the British Armed Forces ‘gay ban’ on 12 January 2020, this book brings together a selection of LGBTQ servicemen and women who have served in the Armed Forces since the Second World War. Their stories are profoundly moving testaments to their loyalty, their courage on the battlefield, and their unswerving sense of right and wrong. Included are ten accounts of members of our Armed Forces who have lived remarkable lives. In some cases they were dismissed in disgrace or forced to resign when asked questions about their private lives. Their stories are those of remarkable sacrifice and courage in their units (and in battle), but who were forced to live in secret before their services were removed at the stroke of a pen after being declared 'no longer required’ or ‘dismissed in disgrace’. These are the stories of the David and Goliath battle for equality, through every court in the UK and Europe. For others their story is one of remarkable careers at the front line of operations worldwide, with accounts of service in the Second World War, the Falklands War, the Gulf Wars and the war in Afghanistan. This book celebrates the lives of servicemen and women who have stood tall and taken their place with pride and dignity in the fighting units of the Royal Navy, Royal Marines, Royal Air Force and the British Army. These are the inspiring stories of people who have created amazing careers and sought and found a welcome denied to so many.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.