1.Burnout among COVID-19 hospital-based contact tracers in Singapore: results of a mixed-method, cross-sectional multicentre study.
Ian Matthias NG ; Tzu-Jung WONG ; Yong YANG ; Indumathi VENKATACHALAM ; Jean Xiang Ying SIM ; Liang En WEE ; Tau Ming LIEW ; Evelyn BOON ; Tong Yong NG ; Hwi Kwang HAN ; Diana Yuen Lan TAN
Singapore medical journal 2025;66(12):651-658
INTRODUCTION:
During the coronavirus disease 2019 (COVID-19) pandemic, contact tracers were under immense pressure to deliver effective and timely contact tracing, raising concerns of higher susceptibility to burnout. Our study aimed to determine burnout prevalence among hospital-based contact tracers and associated risk factors, so that interventions to reduce burnout risk could be formulated.
METHODS:
One hundred and ninety-six active contact tracers across three hospitals within a healthcare cluster were invited to complete an anonymous online survey. To identify burntout, data such as demographics, work-related variables and contact tracing-related variables were collected using the Copenhagen Burnout Inventory. Associated factors were identified using multivariate statistics. Open-ended questions were included to understand the challenges and potential improvements through qualitative analysis.
RESULTS:
A total of 126 participants completed the survey, giving a completion rate of 64%, and almost half of these participants (42.9%) reported burnout. Protective factors included being on work-from-home arrangements (adjusted odds ratio [OR] 0.22, 95% confidence interval [CI] 0.08-0.56), perception of being well supported by their institution (adjusted OR 0.25, 95% CI 0.08-0.80) and being married (adjusted OR 0.28, 95% CI 0.12-0.64). Risk factors included having an administrative role pre-COVID-19 (adjusted OR 3.62, 95% CI 1.33-9.83). Work-related burnout was related to being activated for more than 1 day in the preceding week (unadjusted OR 3.25, 95% CI 1.33-7.94) and multiple activations in a day (unadjusted OR 3.54, 95% CI 1.44-4.41). Biggest challenges identified by participants were language barrier (62.7%), followed by workflow-related issues (42.1%).
CONCLUSION
Our study demonstrated burnout and other challenges faced by a team of mostly hospital-based administrative staff redeployed on a part-time basis to ensure timely contact tracing. To mitigate burnout, we recommend choosing staff on work-from-home arrangements and ensuring adequate manpower and rostering arrangements.
Humans
;
COVID-19/epidemiology*
;
Burnout, Professional/epidemiology*
;
Singapore/epidemiology*
;
Female
;
Male
;
Cross-Sectional Studies
;
Adult
;
Middle Aged
;
Risk Factors
;
Surveys and Questionnaires
;
Contact Tracing/methods*
;
SARS-CoV-2
;
Prevalence
;
Pandemics
2.Knowledge, attitudes and readiness of final-year medical students towards clinical goals-of-care discussion.
Isaac Kah Siang NG ; Wilson Guo Wei GOH ; Christopher Zi Yi THONG ; Li Feng TAN ; Chong Han PEH ; Ken Xingyu CHEN ; Pamela GOH ; Desmond B TEO
Annals of the Academy of Medicine, Singapore 2024;53(12):768-771
5.Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma
Yee Hui YEO ; Jamil S. SAMAAN ; Wee Han NG ; Peng-Sheng TING ; Hirsh TRIVEDI ; Aarshi VIPANI ; Walid AYOUB ; Ju Dong YANG ; Omer LIRAN ; Brennan SPIEGEL ; Alexander KUO
Clinical and Molecular Hepatology 2023;29(3):721-732
Background/Aims:
Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive and personalized care to improve outcomes. ChatGPT (Generative Pre-trained Transformer), a large language model, holds the potential to provide professional yet patient-friendly support. We aimed to examine the accuracy and reproducibility of ChatGPT in answering questions regarding knowledge, management, and emotional support for cirrhosis and HCC.
Methods:
ChatGPT’s responses to 164 questions were independently graded by two transplant hepatologists and resolved by a third reviewer. The performance of ChatGPT was also assessed using two published questionnaires and 26 questions formulated from the quality measures of cirrhosis management. Finally, its emotional support capacity was tested.
Results:
We showed that ChatGPT regurgitated extensive knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The performance was better in basic knowledge, lifestyle, and treatment than in the domains of diagnosis and preventive medicine. For the quality measures, the model answered 76.9% of questions correctly but failed to specify decision-making cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines variations, such as HCC screening criteria. However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis.
Conclusions
We analyzed the areas of robustness and limitations of ChatGPT’s responses on the management of cirrhosis and HCC and relevant emotional support. ChatGPT may have a role as an adjunct informational tool for patients and physicians to improve outcomes.

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