Aim The long-term stress, anxiety and job burnout experienced by healthcare workers (HCWs) are important to consider as the novel coronavirus disease (COVID-19) pandemic stresses healthcare systems globally. The primary objective was to examine the changes in the proportion of HCWs reporting stress, anxiety, and job burnout over six months during the peak of the pandemic in Singapore. The secondary objective was to examine the extent that objective job characteristics, HCW-perceived job factors, and HCW personal resources were associated with stress, anxiety, and job burnout. Method A sample of HCWs (doctors, nurses, allied health professionals, administrative and operations staff; N = 2744) was recruited via invitation to participate in an online survey from four tertiary hospitals. Data were gathered between March-August 2020, which included a 2-month lockdown period. HCWs completed monthly web-based self-reported assessments of stress (Perceived Stress Scale-4), anxiety (Generalized Anxiety Disorder-7), and job burnout (Physician Work Life Scale). Results The majority of the sample consisted of female HCWs (81%) and nurses (60%). Using random-intercept logistic regression models, elevated perceived stress, anxiety and job burnout were reported by 33%, 13%, and 24% of the overall sample at baseline respectively. The proportion of HCWs reporting stress and job burnout increased by approximately 1·0% and 1·2% respectively per month. Anxiety did not significantly increase. Working long hours was associated with higher odds, while teamwork and feeling appreciated at work were associated with lower odds, of stress, anxiety, and job burnout. Conclusions Perceived stress and job burnout showed a mild increase over six months, even after exiting the lockdown. Teamwork and feeling appreciated at work were protective and are targets for developing organizational interventions to mitigate expected poor outcomes among frontline HCWs.

The aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding fish behavior. Fish classification is performed by combining two types of features: 1) Texture features extracted by using statistical moments of the gray-level histogram, spatial Gabor filtering and properties of the co-occurrence matrix and 2) Shape Features extracted by using the Curvature Scale Space transform and the histogram of Fourier descriptors of boundaries. An affine transformation is also applied to the acquired images to represent fish in 3D by multiple views for the feature extraction. The system was tested on a database containing 360 images of ten different species achieving an average correct rate of about 92%. Then, fish trajectories, extracted using the proposed fish classification combined with a tracking system, are analyzed in order to understand anomalous behavior. In detail, the tracking layer computes fish trajectories, the classification layer associates trajectories to fish species and then by clustering these trajectories we are able to detect unusual fish behaviors to be further investigated by marine biologists.

Background: The COVID-19 disease outbreak that first surfaced in Wuhan, China, in December 2019, has taken the world by storm and ravaged almost every country in the world. Emergency departments (ED) in hospitals are on the frontlines, serving an essential function in identifying these patients, isolating them early whilst providing urgent medical care. This outbreak has reinforced the role of Emergency Medicine in public health. This paper documents the challenges faced and measures taken by a tertiary hospital's ED in Singapore, in response to the outbreak. Main body: The ED detected the first case of COVID-19 in Singapore on 22 January 2020 in a Chinese tourist and also the first case of locally transmitted COVID-19 on 3 February 2020. The patient journeys through the patient reception area in the ED and undergoes fever screening before being shunted to isolation areas within the ED. Management and disposition of suspect COVID-19 patients are guided by a close-knit collaboration between ED and department of infectious diseases. With increasing number of patients, backup plans for expansion of space and staff augmentation have been enacted. Staff safety is also of utmost importance, with provision and guidelines for personal protective equipment and team segregation to ensure no cross-contamination across staff. These have been made possible with an early setup of an operational command and control structure within the ED, managing manpower, logistics, operations, communication and information management and liaison with other clinical departments. Conclusion: With the large numbers of undifferentiated patients managed by the ED to date, more than 820 patients with COVID-19 have been identified in the hospital. Not a single member of the staff of the SGH Emergency Department has come down with the illness. The various measures undertaken by the department have helped to ensure good staff morale and strict adherence to safety procedures. We share the lessons learnt so that others who manage EDs around the world can benefit from our experience.

IMPORTANCE Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. OBJECTIVES To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores. DESIGN, SETTING, AND PARTICIPANTS This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. MAIN OUTCOMES AND MEASURESThree SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively.Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curve in the receiver operating characteristic analysis. RESULTSThe study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates inthe training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores. CONCLUSIONS AND RELEVANCEIn this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.

No abstract