QMUL

This project seeks to explore delivery of interpreting services in GP practices for pregnant women who speak no/limited English, as well as the interpreting support provided through this channel to further maternity services. Primary care is the first point of contact for pregnant women to receive maternal care, and effective communication within primary care settings is key to healthcare access, quality of care, and health outcomes. Language barriers contribute to poor maternal health outcomes and inequities. The intern will have an exciting opportunity to contribute to the development of the project, mainly on evidence synthesis and patient and public involvement and engagement (PPIE) processes. This project will be relevant to students from all disciplines.

Primary care research happens and thrives in a collaborative environment. Therefore, beyond the immediate project, the intern will have a great opportunity to be part of our team, comprising of a reader, 2 postdocs and 3 PhD students. We have diverse expertise in primary care, clinical academic research, global health, patient safety and anthropology. We work on diverse projects relating to communication and improvements in primary care using relevant methodologies including evidence synthesis, case studies and ethnography. Our team holds weekly Monday meetings where we share reflections and take turns in chairing; this will be a key learning and leadership opportunity for the intern. We will introduce the intern to other researchers beyond our team, including our departmental colleagues and collaborators. The intern will also attend valuable activities including departmental seminars, online webinars and campus-wide events.

Artificial Intelligence (AI) is rapidly advancing and leading to promising healthcare innovation. Government agendas indicate that AI could transform primary care services. AI technologies have the potential to save time for both GPs and patients, and lead to improved patient outcomes. Whilst the use of AI in GPs is known about, the opinions of clinicians and patients on the use of these tools are not well-understood.  A recent study showed that 20% of General Practitioners (GPs) in the UK use Artificial Intelligence (AI), such as ChatGPT, at work (Blease, 2024), indicating that AI-tools are already becoming assimilated into clinical practice.

The intern will join a project aimed at understanding attitudes towards AI in UK General Practice. The successful intern will help gather online forum data and analyse posts using qualitative research methods. Online forums hold a wealth of potentially useful information on patient and clinician views. There may also be the opportunity to help disseminate results (e.g., at a national AI or clinical conference).

This will be a qualitative study, so knowledge (e.g., from university modules) of and interest in qualitative methods would be beneficial. The intern must be an undergraduate, and the position is open to any degree subject. Examples of previous forum analysis work in healthcare include on IVF and asthma, and this project provides a great opportunity to contribute towards the emerging research literature on AI in healthcare. 

Any further information:

If you have any questions please contact Duncan Reynolds (Duncan.reynolds@qmul.ac.uk) and/or Rebecca Muir (r.l.muir@qmul.ac.uk) for an informal discussion.

Working with an interdisciplinary team on the COMPLEAT project (Chronic Obstructive pulmonary disease-MultiPle Long tErm conditions and Assistive Technology)

The aim of the project is to develop potential solutions with plans for further evaluation in the area of chronic obstructive pulmonary disease-multiple long term conditions (https://www.nice.org.uk/guidance/ng115/chapter/Recommendations#multidisciplinary-management) and assistive technology (https://www.gov.uk/government/publications/assistive-technology-definition-and-safe-use/assistive-technology-definition-and-safe-use) with a view to improving patient outcomes and services).  

The internship project will support learning and delivery of the following proposed research activities (depending on study progress and when the intern is able to join the project):

(1) generation of a visual evidence gap map, an output from ongoing work;

(2) taking the evidence gap map and the output of several interdisciplinary

discussions including with public contributors (part of team training) to a stakeholder workshop; (3) leading to a prioritised list of research areas to address in a future grant application;

(4) development of a digital systems map outline;

(5) research proposal draft outline; and (6) dissemination of findings.

Any further information:

Please contact us for further information as needed: r.sohanpal@qmul.ac.uk s.j.c.taylor@qmul.ac.uk

PROTECT-C (https://www.protect-c.co.uk) is an intervention trial designed to test a population genetic testing approach - offering panel genetic testing to the public to assess whether they carry a pathogenic variant that increases their risk of breast, ovary, bowel, and/or womb cancer.

As part of the trial, we plan to conduct a comprehensive literature review to evaluate socio-ethical issues associated with this population-based approach. This review will critically appraise a wide range of (inter)national guidelines, policy, legal frameworks and literature to generate normative claims and arguments regarding the provision of panel genetic testing to the general population. The normative data will serve as a solid foundation to inform the integration of panel genetic testing into various healthcare settings.

Interns will be involved in the literature review project as second coders. Their responsibilities will include title and abstract screening, full-text screening, and results extraction. Interns will receive training in conducting rigorous systematic reviews. They are expected to read the protocol and attend training sessions to ensure consistency in study interpretation at each stage of screening. Before working independently, they will pilot-screen studies and compare results with the primary coder to identify discrepancies. Regular meetings will be held to discuss disagreements and refine the process. Our goal is to achieve a reliability rate of at least 80% at each stage of screening.