Objective: EDs are highly demanding workplaces generating considerable potential for occupational stress experiences. Previous research has been limited by a focus on specific aspects of the working environment and studies focussing on a range of variables are needed. The aim of the present study was to describe the perceptions of occupational stress and coping strategies of ED nurses and doctors and the differences between these two groups. Methods: This cross-sectional study was conducted at a public metropolitan hospital ED in Queensland, Australia. All ED nurses and doctors were invited to participate in an electronic survey containing 13 survey measures and one qualitative question assessing occupational stress and coping experiences. Descriptive statistics were employed to report stressors. Responses to open-ended questions were thematically analysed. Results: Overall, 104 nurses and 35 doctors responded (55.6% response rate). Nurses reported higher levels of both stress and burnout than doctors. They also reported lower work satisfaction, work engagement, and leadership support than doctors.Compared with doctors, nurses reported significantly higher stress from heavy workload/poor skill mix, high acuity patients, environmental concerns, and inability to provide optimal care. Thematic analysis identified high workload and limited leadership and management support as factors contributing to stress. Coping mechanisms, such as building personal resilience, were most frequently reported. Conclusions: The present study found organisational stressors adversely impact the well-being of ED nurses and doctors. Organisationalfocused interventions including leadership development, strategic recruitment, adequate staffing and resources may mitigate occupational stress and complement individual coping strategies. Expanding this research to understand broader perspectives and especially the impact of COVID-19 upon ED workers is recommended.

Background In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. Methods In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. Results Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. Conclusion We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. Trial Registration numbers Data of following cohorts were used for this project: BACC (www.clinicaltrials.gov; NCT02355457), stenoCardia (www.clinicaltrials.gov; NCT03227159), ADAPT-BSN (www.australianclinicaltrials.gov.au; ACTRN12611001069943), IMPACT (www.australianclinicaltrials.gov.au, ACTRN12611000206921), ADAPT-RCT (www.anzctr.org.au; ANZCTR12610000766011), EDACS-RCT (www.anzctr.org.au; ANZCTR12613000745741); DROP-ACS (https://www.umin.ac.jp, UMIN000030668); High-STEACS (www.clinicaltrials.gov; NCT01852123), LUND (www.clinicaltrials.gov; NCT05484544), RAPID-CPU (www.clinicaltrials.gov; NCT03111862), ROMI (www.clinicaltrials.gov; NCT01994577), SAMIE (https://anzctr.org.au; ACTRN12621000053820), SEIGE and SAFETY (www.clinicaltrials.gov; NCT04772157), STOP-CP (www.clinicaltrials.gov; NCT02984436), UTROPIA (www.clinicaltrials.gov; NCT02060760). Graphical Abstract