The association between rotating shift work and increased occupational stress in nurses: Pei‐Chen Lin, et al. Department of Public Health, College of Health Sciences, Kaohsiung Medical University, TaiwanPurpose: The aim of this study was to investigate whether rotating shift work increases occupational stress in nurses.Methods: This study measured shift work scheduling and occupational stress by using the Effort‐Reward Imbalance model with self‐reported questionnaires in a sample of 654 female nurses.Results: Overcommitment risk was higher in nurses who worked rotating shifts than in those who worked day/non‐night shifts (OR, 2.16; 95% CI, 1.03−4.66). However, an effort/reward imbalance was not directly associated with work schedules (OR, 1.88; 95% CI, 0.87−4.35). Among nurses working rotation rotating shifts, those who had 2 days off after their most recent night shifts showed an alleviated risk of overcommitment (OR, 0.52; 95% CI, 0.32−0.82), but those who had worked for at least one series of 7 consecutive work days per month had an increased risk of effort/reward imbalance (OR, 2.75; 95% CI, 1.69−4.48). Additionally, those who had little or no participation in planning working hours and shift scheduling and worked overtime at least three times per week during the preceding 2 months tended to have high stress.Conclusions: The nurses who worked rotating shifts tended to experience work‐related stress, but their stress levels improved if they had at least 2 days off after their most recent night shift and if they were not scheduled to work 7 consecutive days. These empirical data can be used to optimize work schedules for nurses to alleviate work stress.

Background: Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images. Results: A Resnet101-9 ensemble model was developed for classifying ALL in microscopic images. The proposed Resnet101-9 ensemble model combined the use of the nine trained Resnet-101 models with a majority voting strategy. Each trained Resnet-101 model integrated the well-known pre-trained Resnet-101 model and its algorithm hyperparameters by using transfer learning method to classify ALL in microscopic images. The best combination of algorithm hyperparameters for the pre-trained Resnet-101 model was determined by Taguchi experimental method. The microscopic images used for training of the pre-trained Resnet-101 model and for performance tests of the trained Resnet-101 model were obtained from the C-NMC dataset. In experimental tests of performance, the Resnet101-9 ensemble model achieved an accuracy of 85.11% and an F₁-score of 88.94 in classifying ALL in microscopic images. The accuracy of the Resnet101-9 ensemble model was superior to that of the nine trained Resnet-101 individual models. All other performance measures (i.e., precision, recall, and specificity) for the Resnet101-9 ensemble model exceeded those for the nine trained Resnet-101 individual models. Conclusion: Compared to the nine trained Resnet-101 individual models, the Resnet101-9 ensemble model had superior accuracy in classifying ALL in microscopic images obtained from the C-NMC dataset.