Burnout in healthcare professionals (HCPs) is a multi-factorial problem. There are limited studies utilizing machine learning approaches to predict HCPs' burnout during the COVID-19 pandemic. A survey consisting of demographic characteristics and work system factors was administered to 450 HCPs during the pandemic (participation rate: 59.3%). The highest performing machine learning model had an area under the receiver operating curve of 0.81. The eight key features that best predicted burnout are excessive workload, inadequate staffing, administrative burden, professional relationships, organizational culture, values and expectations, intrinsic motivation, and work-life integration. These findings provide evidence for resource allocation and implementation of interventions to reduce HCPs' burnout and improve the quality of care.

We present evidence on the current state of utilizing co-design approaches involving older adults in developing electronic healthcare tools (EHTs). Research gaps were identified in defining the stages, involvement processes, and levels of participation using existing theoretical frameworks. Future studies should explore both involvement processes and levels of participation to optimally empower and collaborate with older adults in developing EHTs.

Cancer is a disease in which cells acquire autonomous growth, genetic instability, and significant metastatic strength, and is considered one of the most common causes of death worldwide. The most important types of cancer-causing these deaths are lung and colon cancers. Although they are rarely seen at the same time, the rate of metastasis of cancerous cells between these two organs is quite high if not diagnosed early. Histopathological diagnosis and appropriate treatment are the only ways to distinguish cancer types and reduce cancer death rates. The use of artificial intelligence in histopathological diagnosis can also provide experts with significant assistance with less effort, time, and cost. In this study a dataset, containing 25000 histopathological images belonging to 5 classes to classify colon and lung cancer types, was used. In order to obtain successful classification results from this dataset, the versions of the DenseNet algorithm, one of the deep learning algorithms, (DenseNet121, DenseNet169, and DenseNet201) were used firstly. Then, 3 novel models (DenseNet121_Improved, DenseNet169_Improved, and DenseNet201_Improved) were proposed by adding a cut-point layer, an auxiliary layer, and making frozen status improvements to the versions of the DenseNet algorithm. Versions of the DenseNet algorithm and proposed models were trained with stratified k-fold cross-validation technique first on colon cancer containing 2-class histopathological images, then lung cancer containing 3-class histopathological images, and lastly on 5-class histopathological images containing both colon and lung cancer. Finally, classification success rates were obtained. According to the experimental results performed on 3 different datasets, 97.60%, and 98.48% classification success rates in the lung cancer dataset and in both colon and lung cancer datasets were obtained respectively. The best classification success rate was achieved with DenseNet201_Improved, which was recommended with 99.80% in the colon cancer dataset.

Classification and monitoring of microalgae species in aquatic ecosystems are important for understanding population dynamics. However, manual classification of algae is a time-consuming method and requires a lot of effort with expertise due to the large number of families and genera in its classification. The recognition of microalgae species has become an increasingly important research area in image recognition in recent years. In this study, machine learning and deep learning methods were proposed to classify images of 12 different microalgae species in order to successfully classify algae cells. 8 Different novel models (MobileNetV3Small-Lr, MobileNetV3SmallRf, MobileNetV3Small-Xg, MobileNetV3Large-Lr, MobileNetV3Large-Rf, MobileNetV3Large-Xg, MobileNetV3Small-Improved and MobileNetV3Large-Improved) have been proposed to classify these microalgae species. Among these proposed model structures, the best classification accuracy rate was 92.22% and the loss rate was 0.72, obtained from the MobileNetV3Large-Improved model structure. In addition, as a result of the experimental results obtained, metrics such as the confusion matrix, which can meet the experts in the correct diagnosis of microalgae species, were also evaluated. This research may in the future open a new avenue for the development of a cost-effective, highly sensitive computer-based system for the use of image analysis and deep learning techniques for the identification and classification of different microalgae.

PURPOSE: This study aimed to assess the impact of simulation-based training intervention on radiation therapy therapist (RTT) mental workload, situation awareness, and performance during routine quality assurance (QA) and treatment delivery tasks. METHODS AND MATERIALS: As part of a prospective institutional review board-approved study, 32 RTTs completed routine QA and treatment delivery tasks on clinical scenarios in a simulation laboratory. Participants, randomized to receive (n = 16) versus not receive (n = 16) simulation-based training had pre- and postintervention assessments of mental workload, situation awareness, and performance. We used linear regression models to compare the postassessment scores between the study groups while controlling for baseline scores. Mental workload was quantified subjectively using the NASA Task Load Index. Situation awareness was quantified subjectively using the situation awareness rating technique and objectively using the situation awareness global assessment technique. Performance was quantified based on procedural compliance (adherence to preset/standard QA timeout tasks) and error detection (detection and correction of embedded treatment planning errors). RESULTS: < .01), but had no significant impact on mental workload or subjective/objective quantifications of situation awareness. CONCLUSIONS: Simulation-based training might be an effective tool to improve RTT performance of QA-related tasks.

Su.vi.max and nutrinet-santé: lessons from large cohorts. This paper presents two epidemiologic studies in the field of nutrition, implemented in France for the last decades: an intervention trial (SU.VI.MAX) and a web-based prospective cohort study (NutriNet-Santé). The SU.VI.MAX study, a randomised, double-blind, placebo-controlled primary prevention trial, has shown that 7.5 years daily low-dose antioxidant supplementation (vitamins and minerals) lowered the total cancer incidence in men only, not in women. This may be explained by a lower baseline status of certain antioxidants (measured by blood concentration) in men compared to women. Finally, the effect of antioxidant supplementation on the incidence of cancer could depend on baseline antioxidant status (which differs from gender and/or nutritional status) and the health status of subjects (healthy vs cancer high-risk subjects). The NutriNet-Santé cohort is a web-based prospective cohort study launched in 2009 aiming to investigate the relationship between nutrition (nutrients, foods, dietary patterns, physical activity) and health outcomes; and to examine the determinants of dietary patterns and nutritional status (sociological, economic, cultural, biological, cognitive, perceptions, preferences, etc.).

Quality checklists have demonstrated benefits in healthcare and other high-reliability organizations, but there remains a gap in the understanding of design approaches and levels of stakeholder engagement in the development of these quality checklists. This scoping review aims to synthesize the current knowledge base regarding the use of various design approaches for developing quality checklists in healthcare. Secondary objectives are to explore theoretical frameworks, design principles, stakeholder involvement and engagement, and characteristics of the design methods used for developing quality checklists. The review followed the Preferred Reporting Items for Systematic Reviews 2020 checklist. Seven databases (PubMed, APA PsycInfo, CINAHL, Embase, Scopus, ACM Digital Library, and IEEE Xplore) were searched for studies using a comprehensive search strategy developed in collaboration with a health sciences librarian. Search terms included "checklist" and "user-centered design" and their related terms. The IAP2 Spectrum of Participation Framework was used to categorize studies by level of stakeholder engagement during data extraction. Twenty-nine studies met the inclusion criteria for this review. Twenty-three distinct design methods were identified that were predominantly non-collaborative in nature (e.g., interviews, surveys, and other methods that involved only one researcher and one participant at a given time). Analysis of the levels of stakeholder engagement revealed a gap in studies that empowered their stakeholders in the quality checklist design process. Highly effective, clear, and standardized methodology are needed for the design of quality checklists. Future work needs to explore how stakeholders can be empowered in the design process, and how different levels of stakeholder engagement might impact implementation outcomes.

Bu çalışmanın amacı üniversitede çalışanların bakış açısıyla, yöneticilerinin liderlik stillerini inceleyerek, liderlik stilleri ile iş tatmini ve örgütsel bağlılık arasındaki ilişkiyi ve aynı zamanda iş tatmininin liderlik stilleri ile örgütsel bağlılık üzerindeki aracılık etkisini incelemektir. Bunun için, Doğu Anadolu’da bulunan bir üniversitede görev yapan öğretim elemanlarına Çok Faktörlü Liderlik Anketi, İş Tatmini Anketi, Örgütsel Bağlılık Anketi uygulanmıştır. Verilerin analizinde güvenilirlik analizi, faktör analizi, korelasyon analizi, çoklu regresyon analizi ve hiyerarşik regresyon analizi teknikleri kullanılmıştır. Araştırma sonucunda liderlik stilleri ve örgütsel bağlılık arasında pozitif ve anlamlı bir ilişki bulunmuştur. İş tatmininin analize katılması ile liderlik stillerinin örgütsel bağlılığa etkisinin azaldığı ve iş tatmininin kısmi aracılık etkisinin olduğu bulunmuştur (Beta = 0.321; Beta = 0.134).

Crack detection is very important during the inspection of building structures to determine whether they are safe. Therefore, to ensure the reliability and longevity of buildings, it is necessary to have experts periodically carry out building inspections. Building inspection has traditionally been conducted using human-based visual inspection methods as well as artificial intelligence methods that have shown great success in computer vision in recent years. In this study, 9 different models (Xception, VGG16, ResNet101, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and EfficientNetB6), which have shown significant success in the field of artificial intelligence, are discussed to detect and classify cracks in building structures. In addition, a new fusion model structure called Mobile-DenseNet has been proposed by making block cutting and adding auxiliary layers to the MobileNetV2 and DenseNet169 model structures. With this proposed model structure, cracks in concrete structures were classified. A dataset consisting of concrete surface images was used to detect and classify cracks occurring in concrete structures, and a 99.87 % success rate was achieved with the proposed Mobile-DenseNet model in classifying cracks occurring on the concrete surface. The proposed model outperformed the traditional pretrained model structures in the study in terms of the number of transactions, density, features, complexity, and success accuracy.

Neural networks are a state-of-the-art approach that performs well for many tasks. The activation function (AF) is an important hyperparameter that creates an output against the coming inputs to the neural network model. AF significantly affects the training and performance of the neural network model. Therefore, selecting the most optimal AF for processing input data in neural networks is important. Determining the optimal AF is often a difficult task. To overcome this difficulty, studies on trainable AFs have been carried out in the literature in recent years. This study presents a different approach apart from fixed or trainable AF approaches. For this purpose, the activation function cyclically switchable convolutional neural network (AFCS-CNN) model structure is proposed. The AFCS-CNN model structure does not use a fixed AF value during training. It is designed in a self-regulating model structure by switching the AF during model training. The proposed model structure is based on the logic of starting training with the most optimal AF selection among many AFs and cyclically selecting the next most optimal AF depending on the performance decrease during neural network training. Any convolutional neural network (CNN) model can be easily used in the proposed model structure. In this way, a simple but effective perspective has been presented. In this study, first, ablation studies have been carried out using the Cifar-10 dataset to determine the CNN models to be used in the AFCS-CNN model structure and the specific hyperparameters of the proposed model structure. After the models and hyperparameters were determined, expansion experiments were carried out using different datasets with the proposed model structure. The results showed that the AFCS-CNN model structure achieved state-of-the-art success in many CNN models and different datasets.

253 Background: Treatment decision making for older adults with acute myeloid leukemia (AML) is complex. An informed discussion between the patient and physician that incorporates patient values and preferences is crucial to arrive at a personalized treatment plan. We sought to understand the patient experience of treatment decision making and identify challenges to aligning care to patient preferences. Methods: We conducted in-depth interviews with newly diagnosed older (≥60 years) adults with AML and their caregivers following a semi-structured interview guide. Qualitative thematic analysis was used to summarize findings. Results: 16 in-depth interviews were conducted. Major themes emerged including: 1) Patients were overwhelmed and in shock (“I was shocked.” “... like my face was hit with a water hose turned all the way up.” “It’s like you have been hit by a truck.”) 2) Patients felt powerless, without true treatment options (“You don’t feel like you are in the driver seat.” “I didn’t see that there was a decision to be made.” “I mean, I had no choice.” “Either go through chemotherapy or go through dying.”) 3) Patients felt rushed and unprepared to make a treatment decision (“I don’t feel like we were prepared at all. It was all thrown in your face, and you had to decide.” “But the mindset when they went through the typical course of chemo... laying that all on me in a two-hour period and wanting me to decide right there and then, I think that that should not be allowed... you can’t make sound decisions in a two-hour period about your life and whether you are going to live or not.”) 4) Patients predominantly followed physician recommendations for treatment (“What went through my mind was I was told that I had a great doctor who was going to be looking at me and I leave stuff like that up to the professionals.” “They told me what needed to be done and I just let them do it.”) 5) Patients balanced many factors when making treatment decisions including survival, quality of life, and spending time with family (“Dr. [Name], I want to live long and strong.” “And both of us prefer quality for a shorter time than we do a longer time in agony.” “I’m not scared to die... it’s just the fact that I’ll miss everyone.”) 6) Patients desired greater input on care plans (“[I suggest] trying to let the patients have a little bit of input on their treatment plan.” “It has to be catered to the patients making those decisions.”). Conclusions: Older adults with newly diagnosed AML feel overwhelmed, unprepared, and rushed into treatment decisions. No one factor dominated treatment decision making highlighting the need for physicians to assess individual patient priorities to arrive at a treatment plan. Interventions to reduce distress, elicit patient preferences and values, and increase participation in treatment decision making would improve the quality of treatment decisions.

Clinicaltrials.gov (NCT00272428).