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We are delighted to announce that the winners of the EBHC Best Dissertation Prizes for 2022-23 are Nur Hidayati Handayani (left) and Abhirami Gautham (right). Huge congratulations to them both. Read their responses to winning and summaries of their dissertations.

two young women portrait style photos both with long hair, with their photos placed side by side on a blue background

Nur Hidayati Handayani

Winning the Outstanding EBHC Dissertation Prize is like mixing a cocktail of perseverance, resilience, and a generous splash of self-compassion - shaken, not stirred, through adversity. It’s a reminder that the academic journey isn't just about flexing your academic muscles but also about embracing the art of being kind to yourself. I am immensely grateful for the tremendous support I received from my family, my second reviewer, Akhil Jadeja, supervisors, Marta Wanat and Stephanie Tierney, Oxford librarian, Nia Roberts, the course director of the MSc EBHC programme, Annette Plüddemann, Kellogg College staff, friends, and my cat.”

Dissertation: A Thematic Synthesis of the Experiences of Social Prescribing among Patients in the UK

Background: Social prescribing connects patients to non-medical activities or resources in the community to address their non-medical needs, it is implemented in the primary health care setting and funded by the NHS system in the UK. In the social prescribing pathway, patients are directed to link workers to identify suitable solutions for their needs such as art workshops or welfare benefit guidance. Social prescribing marks a notable transition from traditional medical treatments to more comprehensive strategies focusing on holistic health and wellbeing strategies. Insights from patient experiences can improve the development of social prescribing to better meet their needs. This understanding can aid in improving the delivery and outcomes of social prescribing.

Method: A thematic synthesis of qualitative studies (typically using interview, observation or group discussion methodology) explored patients’ experiences of social prescribing in the UK. An exhaustive search was performed on six databases. The CASP tool for qualitative research was used for quality assessment. I analysed data by grouping ideas within the included research papers to develop and understand different patterns across them.

Result: From 2,530 references, 1,261 duplicates were removed. Titles and abstracts of 1,269 papers were screened. In total, 85 papers were full text-screened, and 19 studies were included in the review. Five analytical themes were developed from these papers: (a) Searching for hope in times of adversity; (b) Variability in temporal responsiveness; (c) Feeling supported and empowered by the social prescribing pathway; (d) Sustained change from a positive response; (e) Misalignment producing no response. The study finds that patients might experience lasting advantages from social prescribing if it aligns with their needs and expectations. Results highlighted the importance of matching social prescribing referral with patients’ readiness to engage. Therefore, it is recommended that healthcare professionals evaluate patient suitability prior to beginning a social prescribing referral.


Abhirami Gautham  

“I am incredibly honoured to have won the prize for an outstanding dissertation in evidence-based healthcare. This would not have been possible without the support of my family, especially my husband Kris, who ensured the tea kept flowing throughout the writing process, and my dog Oscar who provided much needed cuddles for stress relief. I am very grateful for all the support I had from my supervisor Dr Jose Ordonez-Mena, and the EBHC team at Oxford. I have had an amazing experience reading the MSc in EBHC, and it has fuelled my passion to translate research into actionable real-world practice, ultimately improving outcomes for patients and healthcare professionals.”

Dissertation: The Accuracy of Computer-Assisted Diagnosis in Identifying Endoscopic and Histological Remission of Ulcerative Colitis in Lower Gastrointestinal Endoscopy: A Systematic Review & Meta-Analysis

Background: Ulcerative Colitis (UC) results in chronic mucosal inflammation of the colon that typically presents as bloody diarrhoea. UC is associated with an increased risk of developing colorectal cancer. Patients with UC typically undergo lower gastrointestinal endoscopy (LGIE) at regular intervals following diagnosis to assess disease activity, response to treatment and cancer surveillance. Endoscopic remission (ER) can be assessed visually during LGIE while histological remission (HR) involves analysis of colonic biopsy samples. Significant inter-operator variability exists in diagnosing ER while histological analysis is associated with increased costs. Computer-aided diagnosis (CAD) is a potential solution to the limitations of current LGIE surveillance in UC. In this study, current literature on the accuracy of CAD was systematically reviewed. A meta-analysis was conducted to provide pooled diagnostic accuracy estimates for CAD when appropriate. 

Methods: Searches were performed on six electronic databases and relevant reference lists. Data was extracted and screened independently by two reviewers. Methodological assessment was performed using the QUADAS-2 tool. Meta-analysis was performed separately for ER and HR studies using the bivariate model. Subgroup analyses were performed to investigate heterogeneity. Overall certainty of evidence was provided based on GRADE methodology.

Results: 21 studies were identified. Some studies assessed ER and HR in the same study population. Following sensitivity analysis, pooled estimates were obtained for 13 ER studies and 5 HR studies. CAD had a pooled sensitivity of 90.3% and specificity of 94.6% for diagnosing ER, and a pooled sensitivity of 90.6% and specificity of 76.8% for diagnosing HR in LGIE.  CAD has high sensitivity and specificity for diagnosing ER, and high sensitivity for diagnosing HR in LGIE. CAD can be incorporated into clinical practice to reduce inter-observer variability in diagnosing ER, and reduce costs associated with histological analysis of colonic mucosa. CAD can also be used to help train young endoscopists in UC diagnoses.