Stress Recognition Research Articles

VBNC Cronobacter sakazakii survives in macrophages by resisting oxidative stress and evading recognition by macrophages

Survival in host macrophages is an effective strategy for pathogenic bacterial transmission and pathogenesis. Our previous study found that viable but non-culturable (VBNC) Cronobacter Sakazakii (C. sakazakii) can survive in macrophages, but its survival mechanism is not clear. In this study, we investigated the possible mechanisms of VBNC C. sakazakii survival in macrophages in terms of environmental tolerance within macrophages and evasion of macrophages recognition. The results revealed that VBNC C. sakazakii survived under oxidative conditions at a higher rate than the culturable C. sakazakii. Moreover, the stringent response gene (relA and spoT) and the antioxidant-related genes (sodA, katG, and trxA) were up-regulated, indicating that VBNC C. sakazakii may regulate antioxidation through stringent response. On the other hand, compared with culturable C. sakazakii, VBNC C. sakazakii caused reduced response (Toll-like receptor 4) in macrophages, which was attributed to the suppression of biosynthesis of the lipopolysaccharides (LPS). Furthermore, we found that ellagic acid can reduce the survival rate of bacteria in macrophages by improving the immune TLR4 recognition ability of macrophages. In conclusion, VBNC C. sakazakii may survive in macrophages by regulating oxidative tolerance through stringent response and altering LPS synthesis to evade TLR4 recognition by macrophages, which suggests the pathogenic risk of VBNC C. sakazakii.

Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine learning

Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine learning

Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis.

Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments. This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings. We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success. Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes. Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions.

Effectiveness of the Huddles in Improving the Patient Safety Attitudes Among Clinical Team Members.

Huddles among members of interdisciplinary medical teams involve short stand-up sessions and allow team members to focus on existing or emerging patient safety issues, thereby facilitating team communication. Hospital managers are able to recognize the current situation of the organization through patient safety attitudes, strengthen team members' awareness of patient safety, and improve the quality of health care. The purpose of this study was to determine the effects of huddles on improving team members' attitudes toward patient safety. We used a quasi-experimental design and selected 2 adult wards with similar properties as the experimental and comparison groups by convenience sampling. Data collection was from December 1, 2021, to June 30, 2022, at a teaching hospital in central Taiwan. Team members of the ward performing huddles formed the experimental group, and they participated 2 times per week in 15-minute huddles from 8:15 to 8:30 am for a total of 4 weeks. The comparison group adopted the routine team care process. Both groups completed the Safety Attitudes Questionnaire during the pre- and post-tests of the study. The experimental group scored significantly higher in the post-test than in the pre-test in all aspects of safety attitudes, with the exception of stress recognition. These improved aspects were teamwork climate (76.47 ± 15.90 vs 83.29 ± 13.52, P < .001), safety climate (75.94 ± 16.14 vs 82.81 ± 13.74, P < .001), job satisfaction (74.34 ± 20.22 vs 84.40 ± 17.22, P <.001), perceptions of management (78.02 ± 19.99 vs 85.51 ± 15.97, P < .001), and working conditions (78.85 ± 17.87 vs 86.81 ± 14.74, P < .001). Through the huddles, clinical team members improved their understanding of different aspects of safety attitudes. Such a study provided ward units with real-time improvement and adjustment in terms of patient safety during their medical work processes with better patient safety.

Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer Rates and pHs.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. 'Supercascade Red', 'Wave© Purple', and 'Carpet Blue' Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for 'Carpet Blue' and 'Wave© Purple' (p > 0.05), though 'Supercascade Red' was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in 'Wave© Purple' (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

Facial Video-Based Non-Contact Stress Recognition Utilizing Multi-Task Learning With Peak Attention.

Negative emotional states, such as anxiety and depression, pose significant challenges in contemporary society, often stemming from the stress encountered in daily activities. Stress (state or level) recognition is a crucial prerequisite for effective stress management and intervention. Presently, wearable devices have been employed to capture physiological signals and analyze stress states. However, their constant skin contact can lead to discomfort and disturbance during prolonged monitoring. In this paper, a peak attention-based multitasking framework is presented for non-contact stress recognition. The framework extracts rPPG signals from RGB facial videos, utilizing them as inputs for a novel multi-task attentional convolutional neural network for stress recognition (MTASR). It incorporates peak detection and HR estimation as auxiliary tasks to facilitate stress recognition. By leveraging multi-task learning, MTASR can utilize information related to stress physiological responses, thereby enhancing feature extraction efficiency. For stress recognition, two binary classification tasks are applied: stress state recognition and stress level recognition. The model is validated on the UBFC-Phys public dataset and demonstrates an accuracy of 94.33% for stress state recognition and 83.83% for stress level recognition. The proposed method outperforms the dataset's baseline methods and other competing approaches.

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition

Stress is a prevalent issue in modern society which affects a large percentage of the population. Stress has negative effects on human daily lives such as reduced memory and concentration levels, forgetfulness, restlessness, and weakness. Although it is challenging to extinguish stress from human lives, monitoring and controlling its consequences can be implemented. Current methods for stress monitoring often lack interpretability of results and making it difficult to provide an interpretable system for immediate mental health counseling and intervention. Wearable devices are of growing interest due to the impact of the COVID-19 pandemic on mental health, as wearables provide monitoring of physiological parameters which helps in measuring stress levels. Here, an investigation on the feasibility of using wrist and chest-based multimodal physiological signals collected from wearables is executed for stress classification and identification of underlying modalities that impact the stress. Multimodalities from wrist and chest sensors are used to examine the three-class classification, baseline, stress and amusement. For improved and visualized effects, the multimodal signals from wearables were transformed into images. Then, a hybrid model was introduced, which comprised of attentive convolution neural network (CNN), transposed attentive CNN connections with long-short term memory (LSTM) and followed by attention layer for efficient feature extraction and improved classification accuracy. Results found that the proposed model with images provided 97 % accuracy and 96 % F1-score for chest wearable and 94 % accuracy and 93 % F1-score for wrist wearable data. Additionally, the post-hoc explainability approach (Local interpretable model agnostic applications, LIME) provided visual representations of contributing features from each signal for each class. LIME shows electrodermal activity (EDA), temperature (TEMP) and respiration (RESP) to be the significant factor in stress recognition from chest wearable and blood volume pulse (BVP) and EDA from wrist wearable. Additionally, increasing the number of features of the explainable model influences the modalities influence on the model explainability. The results establish a benchmark for explainable stress recognition using different sensor data.

Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

Entertainment robots based on internet of things and artificial intelligence in the process of student stress and emotion recognition

For different entertainment robot tasks and action requirements, this article adopts multiple control algorithms and optimizes the algorithms. There are certain errors in the movement and interaction process of entertainment robots, which can affect the control effect and user experience of the robots. Therefore, the study models and analyzes the errors of robots by establishing error models, and proposes corresponding correction strategies to correct the errors, thereby improving the control accuracy and reliability of entertainment robots. In terms of identifying and alleviating student stress emotions, multimodal fusion comprehensively utilizes multiple sensors and data sources to obtain emotional information of students, more accurately identifying and understanding their stress emotions. Based on these analysis results, entertainment robots can adopt corresponding relief strategies to help students alleviate stress emotions. By comparing the changes in stress emotions and student feedback before and after the experiment, entertainment robots based on the Internet of Things and artificial intelligence have potential application value in the process of student stress emotion recognition. The remote control system and optimized control effect of robots provide flexibility and efficiency for robot operation. The evaluation results of stress relief effect show that entertainment robots have a positive impact on the relief of student stress emotions.

Differentially Private federated learning to Protect Identity in Stress Recognition

Differentially Private federated learning to Protect Identity in Stress Recognition

Testing the effectiveness of the culturally adapted skills training START NOW to reduce mental health problems in adolescent refugees: study protocol for a randomized controlled trial.

Adolescent refugees are particularly vulnerable to mental health problems, as they experience many risk factors associated with their resettlement at crucial stages of their physical and emotional development. However, despite having a greater healthcare needs than others, they face significant barriers to accessing healthcare services. Therefore, this study aims to test the effectiveness of a low-threshold, culturally adapted version of the skills training START NOW - START NOW Adapted - in reducing mental health problems among adolescent refugees. We will recruit 80 adolescent refugees (15-18 years) with symptoms of anxiety and depression or high perceived stress in Northwestern Switzerland. They will be randomly assigned to one of two study groups: an intervention group, receiving START NOW Adapted, and a control group, receiving treatment as usual (TAU). The intervention will last 10 weeks and will consist of one-hour sessions per week provided by a trained facilitator with the same cultural background, in the respective language. Assessments to collect depressive and anxious symptoms, perceived stress, social-ecological resilience, and emotion recognition abilities will be conducted pre-intervention, post-intervention (11 weeks later) and at the 3-month follow-up. Multilevel models will be computed with primary and secondary outcome measures as dependent variables. An effect of at least moderate size will be considered clinically relevant. This randomized controlled trial aims to investigate the effectiveness of a culturally adapted version of START NOW, providing valuable insights to improve current health promotion for adolescent refugees in Switzerland (or rather lack thereof). Ultimately, the effects of START NOW may facilitate integration and promote healthy development while decreasing costs associated with treating migration- or conflict-related trauma.Clinical trial registration: ClinicalTrials.gov, identifier: NCT06324864.

Attitude toward perioperative safety among operation room clinicians at Ethiopian University Hospital.

Patient safety is a fundamental of good quality and also a high priority for the health-care system. Maintaining patient safety reduces errors and harm that patients can suffer during health care. The operating room clinicians have a vital role in ensuring patient safety. The general objective of this study was to assess attitudes towards perioperative safety and associated factors among the operation room clinicians at the University of Gondar Comprehensive Specialized Hospital (UoGCSH), Northwest Ethiopia, 2022. A cross-sectional study was conducted on operation room clinicians at UoGCSH. The data were collected by using a self-administered structured questionnaire that included the Safety Attitude Questionnaire (SAQ). Binary logistic regression analysis was employed, and the strength of association was described in adjusted odds ratios with a 95% confidence interval (CI). A total of 260 (76% response rate) operation room clinicians have participated in this study. The mean ± SD of attitude toward perioperative safety was 57.8 ± 0.9. Only 32 (12.3%) operation room clinicians have shown a favorable attitude toward perioperative safety. Most of the clinicians were found to have unfavorable attitudes toward all domains of SAQ except the stress recognition domain. Age >30 years [adjusted odds ratios (AOR): 3.1, CI: 1.1, 8.7, P = .035], working for ≥40 h/week (AOR: 3.9, CI: 1.4, 11.1, P = .01), working in ophthalmologic (AOR: 12.0, CI: 3.8, 38.8, P < .001) and gynecologic (AOR: 3.6, CI: 1.1, 12.7, P = .04) operation rooms, and having training on perioperative safety (AOR: 2.6, CI: 1.1, 6.5, P < .03) were found associated with having favorable attitude toward perioperative safety. Most operation room clinicians had an unfavorable attitude toward perioperative safety and all the domains of SAQ except the stress recognition domain. Older age ≥ 30 years, working for ≥40 h/week, having safety-related training, and working in ophthalmologic and gynecologic operation rooms were found associated with having a favorable attitude toward perioperative safety.

Sentiment Analysis-Based Automatic Stress and Emotion Recognition using Weighted Fused Fusion-Based Cascaded DTCN with Attention Mechanism from EEG Signal

When loaded with difficulties in fulfilling daily requirements, a lot of people in today’s world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre- processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional Variational Autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are optimised using the Adaptive Egret Swarm Optimisation Algorithm (AESOA) so that the weighted fused features are obtained from these two sets of extracted features. Then, a Cascaded Deep Temporal Convolution Network with Attention Mechanism (CDTCN-AM) is used to recognise stress and emotion. The validation of the results from the developed stress and emotion recognition approach is carried out against traditional models in order to showcase the effectiveness of the suggested approach.

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.

Effect of interventions to improve safety culture on healthcare workers in hospital settings: a systematic review of the international literature

BackgroundIn an era of safety systems, hospital interventions to build a culture of safety deliver organisational learning methodologies for staff. Their benefits to hospital staff are unknown. We examined the...

Sex differences of the association between handgrip strength and health-related quality of life among patients with cancer

The purpose of this study is to investigate the association between handgrip strength (HGS) and health-related quality of life (HRQoL), demonstrating HGS as an effective indicator for evaluating HRQoL of patients with cancer. Analyzing 1657 Korean adult cancer patients (644 males, 1013 females) aged ≥ 20 years from the Korea National Health and Nutrition Examination Survey (2014–2019), HGS was standardized based on body mass index and categorized by sex. HRQoL was assessed using the Euro Quality of Life-5-Dimension 3-Level version (EQ-5D-3L) Index. Lower relative HGS was associated with decreased HRQoL in female patients, while no significant association was found in male patients. The lowest quartile of relative HGS exhibited a 2.5-fold decrease in HRQoL compared to the highest quartile (OR 2.50, 95% CI 1.59–3.95, p < 0.001). Both male and female patients with cancer were affected by age, subjective health perception, and stress recognition regarding HRQoL. This study suggests that HGS may be associated with the HRQoL of female patients with cancer, emphasizing that the HGS measurement can be effectively utilized as a pivotal tool for evaluating HRQoL in female patients with cancer.

Exploring attitudes toward safety climate: a cross-sectional study among interventional radiology team members using the Safety Attitudes Questionnaire (SAQ)

BackgroundInterventional radiology (IR) has evolved into a well-established medical discipline known for its minimally invasive procedures. Establishing a culture of safety in IR settings is crucial to improve patient safety and quality of care. This study describes and compares attitudes about patient safety among professionals working with IR in Sweden. In addition, this study evaluates whether the WHO Safety Checklist for IR is routinely used for radiological interventions.MethodsThis cross-sectional survey uses an electronic questionnaire to collect data from 25 IR units. The attitudes of IR team members (radiographers, physicians, nurses, and assistant nurses) were assessed using the Swedish Safety Attitudes Questionnaire (SAQ), which was adapted for this context.ResultsThe two highest factor scores/percentages of positive responses from the IR team were found for job satisfaction (90/94%) and teamwork climate (85/92%), and stress recognition had the lowest values (54/25%). The attitudes were relatively consistent, with few significant differences between the professions (Factor scores: P = 0.039 for job satisfaction, and P = 0.050 for working conditions. Percentage of positive responses: P = 0.027 for perceptions of management). The radiographers tended to have slightly lower values compared to the other professions among these factors (P < 0.05). The WHO safety checklist for IR was not being used on a regular basis, but the respondents recognized its importance for patient safety.ConclusionsThis study shows high factor scores for the different safety climate factors, with job satisfaction having the highest values. Areas that need improvement are perceptions of management and stress recognition. Attitudes were consistent across professions in the IR team, but radiographers had slightly lower values in some aspects.

Dynamic Alignment and Fusion of Multimodal Physiological Patterns for Stress Recognition

Dynamic Alignment and Fusion of Multimodal Physiological Patterns for Stress Recognition

Validation study of the Amharic version Safety Attitudes Questionnaire (SAQ) in public hospitals of Addis Ababa, Ethiopia: a cross-sectional study

BackgroundIn Ethiopia, there is a growing concern about improving patients’ safety in healthcare facilities. However, the lack of a valid and reliable instrument sensitive to the Ethiopian culture for measuring health professional practice environment leads to difficulty in constructing evaluations of safety climate and further linking organizational research to outcomes research. This research study examined the psychometric properties of the Safety Attitude Questionnaire (SAQ) in the Amharic language within an Ethiopian healthcare context.MethodA hospital-based cross-sectional study design was conducted. The SAQ was meticulously translated into Amharic using forward and backward translation methods. Content validity was evaluated with input from seven patient safety and healthcare quality experts. Face validity was established through feedback from healthcare professionals. Then, the Amharic SAQ (SAQ-A) was distributed to 648 participants working in 11 public hospitals, and a total of 611 valid questionnaires were completed and returned (95.2% response rate). Cronbach’s alpha, McDonald’s omega, composite reliability, correlation analysis, and average variance estimation were calculated, and confirmatory factor analysis was performed. Descriptive analyses were performed to describe socio-demographic characteristics. A P-value of ≤0.05 was considered statistically significant. Tables, figures, charts, and texts are used for data presentation.ResultThe overall internal consistency (Cronbach’s alpha) for the 31-item SAQ-A was 0.903, indicating excellent reliability. Confirmatory factor analyses demonstrated a good model fit for each dimension and the entire construct (χ2=1086.675, df=412, p<0.001, comparative fit index (CFI)=0.923, Tucker Lewis index (TLI)=0.913, and root mean square error of approximation (RMSEA)=0.052). The positive response rate of healthcare workers in hospitals was 32.1%. The positive response rates of the six dimensions were teamwork climate (59.7%), safety climate (41.9%), job satisfaction (57.1%), working conditions (37.5%), perception of management (37.6%), and stress recognition (46.2%).ConclusionThe Amharic translation of the SAQ showed good psychometric properties, making it a valuable tool for assessing safety attitudes among Amharic-speaking Ethiopian healthcare practitioners.