COVID-19 adaptations to a training and support programme to improve primary care response to domestic abuse: a mixed methods rapid study.

Background: Increased incidence and/or reporting of domestic abuse (DA) accompanied the COVID-19 pandemic. National lockdowns and enforced social isolation necessitated new ways of supporting victims of DA remotely. Identification and Referral to Improve Safety (IRIS) is a programme to improve the response to domestic abuse in general practice, providing training for general practice teams and support for patients affected by DA, which has previously been proven effective and cost-effective [1-3].

Adapting domestic abuse training to remote delivery during the COVID-19 pandemic: a qualitative study of views from general practice and support services.

Background: Identifying and responding to patients affected by domestic violence and abuse (DVA) is vital in primary care. There may have been a rise in the reporting of DVA cases during the COVID-19 pandemic and associated lockdown measures. Concurrently general practice adopted remote working that extended to training and education.

Rapid readout of terahertz orbital angular momentum beams using atom-based imaging.

We demonstrate the rapid readout of terahertz orbital angular momentum (OAM) beams using an atomic-vapor-based imaging technique. OAM modes with both azimuthal and radial indices are created using phase-only transmission plates. The beams undergo terahertz-to-optical conversion in an atomic vapor, before being imaged in the far field using an optical CCD camera.

General practice wide adaptations to support patients affected by DVA during the COVID-19 pandemic: a rapid qualitative study.

Background: Reporting of domestic violence and abuse (DVA) increased globally during the pandemic. General Practice has a central role in identifying and supporting those affected by DVA. Pandemic associated changes in UK primary care included remote initial contacts with primary care and predominantly remote consulting.

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

Background: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborious and time-consuming when contoured manually. Artificial intelligence (AI)-based auto-segmentation has the potential to significantly accelerate the radiation therapy treatment planning process; however, the accuracy of auto-segmentation needs to be validated before its full clinical adoption.