James Hodgkinson

Nov 28, 2024 | bmcpregnancychildbirth.biomedcentral.com | Alison Chisholm |Sheila Greenfield |James Hodgkinson |Layla Lavallee |Paul D. Leeson |Lucy H Mackillop | +9 more

The COVID-19 pandemic accelerated the adoption of remote care, or telemedicine, in many clinical areas including maternity care. One component of remote care, the use of self-monitoring of blood pressure in pregnancy, could form a key component in post-pandemic care pathways. The BUMP trials evaluated a self-monitoring of blood pressure intervention in addition to usual care, testing whether it improved detection or control of hypertension for pregnant people at risk of hypertension or with hypertension during pregnancy. This paper reports the qualitative evaluation which aimed to understand how the intervention worked, the perspectives of participants in the trials, and, crucially, those who declined to participate. The BUMP trials were conducted between November 2018 and May 2020. Thirty-nine in-depth qualitative interviews were carried out with a diverse sample of pregnant women invited to participate in the BUMP trials across five maternity units in England. Self-monitoring of blood pressure in the BUMP trials was reassuring, acceptable, and convenient and sometimes alerted women to raised BP. While empowering, taking a series of self-monitored readings also introduced uncertainty and new responsibility. Some declined to participate due to a range of concerns. In the intervention arm, the performance of the BUMP intervention may have been impacted by women’s selective or delayed reporting of raised readings and repeated testing in pursuit of normal BP readings. In the usual care arm, more women were already self-monitoring their blood pressure than expected. The BUMP trials did not find that among pregnant individuals at higher risk of preeclampsia, blood pressure self-monitoring with telemonitoring led to significantly earlier clinic-based detection of hypertension nor improved management of blood pressure. The findings from this study help us understand the role that self-monitoring of blood pressure can play in maternity care pathways. As maternity services consider the balance between face-to-face and remote consultations in the aftermath of the COVID-19 pandemic, these findings contribute to the evidence base needed to identify optimal, effective, and equitable approaches to self-monitoring of blood pressure.

Oct 23, 2024 | wessexscene.co.uk | James Hodgkinson

Illustrated by Radu SegaQueer writing is viewed by many as a relatively new space within the world of literature. Indeed, it was not until the 20th century that writers were able to freely express queerness through their works, with pioneers such as Virginia Woolf and James Baldwin writing about queerness with an unprecedented transparency. Yet queer people have existed for much longer, and have been writing their stories for just as long.

Aug 20, 2024 | splunk.com | James Hodgkinson

When we hunt in new environments and datasets, it is critical to build an understanding of what they contain, and how we can leverage them for future hunts. For this purpose, we recommend the PEAK Threat Hunting Framework's baseline hunting process.

Aug 13, 2024 | splunk.com | James Hodgkinson

As outlined in a previous post, OpenTelemetry and Splunk Observability Cloud can provide great visibility when security teams investigate activity in modern environments. In this post, we look at another aspect of this visibility: how you can use traces to see directly into the workings of an application to find a potential threat. Let’s imagine we’re the security analyst, and a message comes across from the Security Operations Center (SOC).

Aug 6, 2024 | splunk.com | James Hodgkinson |Shannon Davis

The Wonderful World of AIAs a small kid, I remember watching flying monkeys, talking lions, and houses landing on evil witches in the film The Wizard of Oz and thinking how amazing it was. Once the curtain pulled back, exposing the wizard as a smart but ordinary person, I felt slightly let down. The recent explosion of AI, and more specifically, large language models (LLMs), feels similar. On the surface, they look like magic, but behind the curtain, LLMs are just complex systems created by humans.