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Dundee Stress State Questionnaire Pdf EXCLUSIVE


This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.




Dundee Stress State Questionnaire Pdf



Simulation sessions can produce high-fidelity emergency situations that facilitate the learning process. These sessions may also generate a complex stress response in the learners. This prospective observational study assessed psychological, physiological, immunological, and humoral levels of stress during high-fidelity simulation training. Fifty-six undergraduate medicine students who took part in a medical simulation session were assigned team roles (physician, nurse or assistant). Subsequently, each participant was assessed before the scenario (T0), after the procedure (T1), and two hours later (T2). Psychological stress and anxiety were measured at T0 and T1, using the State-Trait Anxiety Inventory (STAI) and Dundee Stress State Questionnaire (DSSQ). Cortisol, testosterone, secretory immunoglobulin class A (sIgA), alpha-amylase, and oxygen saturation level were measured at T0, T1, and T2, as was the physiological response indicated by heart rate (HR) and blood pressure (BP). It was found that the onset of task performance was related to increased anticipatory worry and higher oxygen saturation. The participants reported decreased worry, followed by increased emotional distress after the simulation training (T1). Participants trait anxiety predicted the intensity of worry, distress and task engagement. In contrast, no clear relationships were found between trait anxiety and biological stress markers. Testosterone levels were growing significantly in each phase of measurement, while physiological responses (BP, HR) increased at T1 and declined at T2. The levels of stress markers varied depending on the assigned roles; however, the trajectories of responses were similar among all team members. No evidence for prolonged cortisol response (T1, T2) was found based on psychological stress at the onset of simulation (T0). Regression analysis followed by receiver operating characteristics analyses showed uncertain evidence that initial state anxiety and worry predicted the levels of sIgA. Medical students are relatively resilient in terms of stress responses to medical simulation. The observed stress patterns and interrelationships between its psychological, physiological, hormonal, and immunological markers are discussed in accordance with theoretical concepts, previous research work, and further recommendations.


Clinical simulations are highly advanced active teaching methods that employ technology tools and produce high-fidelity scenarios, providing a beneficial learning environment in aviation or medicine. According to worldwide data, iatrogenic complications are the leading cause of death1, while simulation techniques enable medical students to learn how to manage emergency events under time pressure with high realism and no exposure to real risk at the same time. Furthermore, high-fidelity simulation (HFS) may effectively contribute to identifying and reducing the causes of human error in medical settings2. Although medical simulations are recognized as a highly cost-effective educational method, there is growing evidence showing that its participants are exposed to stress, which manifests in a complex way, including physiological response, humoral reactions, or psychological state of distress3,4,5.


Low task engagement (lack of motivation), negative cognitions (worrying) and negative emotional appraisal (distress and state anxiety) at the onset of the task (T0) predict humoral response indicated by cortisol, testosterone, α-amylase and IgA levels.


Regarding changes in psychological stress and anxiety between T0 and T1, the mean scores were compared with each other; however, no directional hypothesis was stated for several reasons. At the onset of a challenging situation, emotional distress and worry could be related to anticipation and primary appraisal, which contributes to perceiving a stressor as a threat18. Considering the individual differences, even when the task is finished and an external stressor disappears, according to Hobfoll25 conservation of resources theory, his or her stress may still escalate. It may occur due to their perceived loss of personal resources related to uncertain outcome or unsuccessful coping with situational demands. Considering the retrospective nature of internal stressors and principles derived from the conservation of resources hypothesis, we did not expect the overall strong decrement of psychological stress immediately after completing the task. The analyses were controlled for sex, previous experience with high-fidelity medical simulation and the assigned role.


Immediately after the end of the scenario (T1), approximately 40 min after T0, all stress levels were measured again, including psychological questionnaires. After the participants were placed sitting at rest for 120 min, their physiological, humoral, and immunological stress markers were measured again (T2). During sitting at rest, participants were briefly informed on the proper interventions without formal debriefing or giving information on individual performance. We allowed participants to exchange their opinions instead.


The receiver operating characteristics (ROC) curve for screening for sIgA at T2, using state anxiety and psychological indicators of stress at T0. Description: Receiver-operating characteristics displaying the ability of state anxiety, worry, distress and task engagement at T0 to predict secretory immunoglobulin class A (sIgA) level (high vs low) at T1. From this data, cutoff point can be generated to determine the greatest sensitivity and specificity for accuracy in terms of state anxiety raw score to predict decreased sIgA, indicating immunological stress response. This original figure was produced by authors who performed statistical computations using IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY. IBM Corp. granted licenses to use SPSS statistical outputs by the corresponding author, representing the institution (University of Lodz).


The third hypothesis was only partially confirmed. It should be noted that the explanatory power of the proposed regression model is too low for precise predictions and that the relationships between psychological stress indicators were in most cases unrelated to cortisol, adrenaline, and α-amylase levels measured at different observation stages of. It has been established that lymphocyte production and activity (responsible for IgA secretion) may be shortly elevated when stressor occurs, and then inhibited due to glucocorticoid levels (a component of the secondary response to stress)8,9. Hence, the variability in sIgA secretion could be attributed to psychological factors, which have been reviewed in literature through the lens of the paradigm of immunological stress markers38. Several studies confirmed the negative relationship between stress and sIgA secretion39,40, which suggests that stress and anxiety may contribute to higher vulnerability of the immune defense system. This pathway by which anxiety could increase susceptibility cannot be extrapolated to long term effects on vulnerability to infectious disease as (a) sIgA levels may have increased after the observation, and (b) state anxiety at T0 may have been affected by many extensive factors outside the context of medical simulation. In general, the evidence from this study indicates that sIgA secretion may be considered as a weak immune marker of stress.


Abstract:The primary objective of this study was to investigate the effects of cyberbullying through social exclusion and verbal harassment on emotional, stress, and coping responses. Twenty-nine undergraduate students (16 females aged 18.25 0.58 years and 13 males aged 18.46 1.13 years) volunteered for the study. All volunteers participated in two experiments that stimulated cyberbullying through social exclusion or verbal harassment. In the first experiment, the effects of cyberbullying through social exclusion were investigated using a virtual ball-tossing game known as Cyberball. In the second experiment, the influence of cyberbullying through verbal harassment was tested using a hypothetical scenario together with reading of online comments. Emotional, stress, and coping responses were measured via the Positive Affect and Negative Affect Scale, the Dundee Stress State Questionnaire, and the Coping Inventory for Task Stress, respectively. The results demonstrated that social exclusion and verbal harassment induced a negative emotional state. We also found that verbal harassment through the use of impolite language increased engagement, and increased worry compared with social exclusion effects.Keywords: cyberbullying; emotions; bullying; social ostracism; stress; social exclusions; harassment 350c69d7ab


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