Chapter One General Introduction (original) (raw)
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Responses to the Zung Self-Rating Anxiety Scale (SAS: Zung, W. (1971). A rating instrument for anxiety disorders. Psychosomatics, 12, 371–379), the Self-Rating Depression Scale (SDS: Zung, W. (1973). From art to science: The diagnosis and treatment of depression. Archives of General Psychiatry, 29, 328–337) and the Fatigue Severity Scale (FSS) developed by Krupp and colleagues (Krupp, L.B., LaRocca, N.G., Muir-Nash,
Exploring Students’ Fatigue: Is there a Relationship between Outcome with Effort and Performance?
International journal of academic research in business & social sciences, 2023
Research on students' fatigue in universities may emerge as one of the interesting fields of exploration in higher education for some reasons. It is associated with unfavourable outcomes such as diminished engagement, achievement and motivation, which may result in dropout. By investigating the connection between students' effort and their outcome and performance, this study aims to shed light at identifying the perception of learners on burnout as the outcome of effort and how it might affect students' performance. The sample size consisted of 100 students of UiTM Centre of Foundation Studies from different programmes namely Engineering, Science, TESL and Law. The quantitative survey used in this study is adapted from the instrument of 5 Likert-scale pioneered by Vroom (1964), Campos,et.al (2011) and Pintrich & De Groot (1990), and comprises six sections: Demographic Profiles, Motivational Scales, Expecting Component, Affective Component, Burnout-Exhaustion and Burnout-Disengagement. A total of 40 items were employed in this survey. The findings reveal that there is significantly low relationship between outcome (burnout) and effort, effort and performance as well as performance and outcome (burnout), respectively. This study is believed to has further significant implication for both educators and learner if the practical consequences for reducing academic burnout and increasing students' selfdetermination are expanded.
School Fatigue and the Factors that Influence IT
2017
In this study, we will underline the phenomenon of school fatigue which has arisen in students from technical colleges. We interviewed 108 students from a technical college in Bacau and 91 students from Suceava. The young people completed a questionnaire with questions about the frequency of fatigue, passive resting and leisure (television and computer use). Fatigue is often present (34.17%), especially in students from Suceava, which suggests that there is a reduced sleep time per night (less than 7 hours). Most young people spend a lot of time in front of the TV screen (4-5 hours a day - 14.57%) and on the computer (4-5 hours a day - 20.10%), which exacerbates fatigue. In adolescents, current ways of spending leisure time should be carefully monitored, as there is a risk of excessive use, leading to the effects of overloading and computer addiction.
Scholar fatigue should be carefully evaluated in order to interfere when necessary. Goals: the evaluation of the differences/similarities regarding fatigue in pupils studying at different high schools. Material and methods: the study was conducted with a group of 237 teenagers studying at three different high schools in Iasi: the Sport High School (75 teenagers), the Music High School (73 students) and the Grammar School (89 students). The pupils completed a questionnaire regarding the emergence of fatigue. The results were analysed using the Pearson CHI Square test. Results and discussions: in 45.99% of cases students were often tired, the calculated differences being statistically significant for a p<0.01 (f=4, χ²=15.500); fatigue was often acknowledged by the grammar school teenagers. The phenomenon appeared in the middle of the week (48.10% p>0.001 f=4, χ²=20.862) and at midday (43.03%, p<0.01, f=4, χ²=11.738). The statistically significant differences show a high frequency of positive answers for grammar school students. One of the factors favouring the appearance of fatigue was the low number of sleeping hours (6-7 hours in 75.94%). The calculated differences were statistically significant for a p<0.001 (f=4, χ²=21.716) and show a high frequency of grammar school teenagers who have little sleep. Conclusions: the appearance of fatigue is different for each high school, which demonstrates specific details of the various features of teenagers’ loads.
FATIGUE EXPERIENCED BY STUDENTS IN A DAY LONG CLASS: A SURVEY ON STUDENTS
Publication Impact Factor (PIF) :1.026 www.sretechjournal.org Abstract: This paper discusses the various factors affecting the performance of a student in a day. A survey was carried out inorder to formulate an opinion about the topic. Here an effort hasbeen made to gather information from students currently pursuing engineering across various colleges having substantial variation in their timetable, in the state of Karnataka as well as at anationallevel. The method adopted to conduct the survey was questionnaire. The findings of the survey indicated thatthe mainfactors affecting students' active listening during lecture are the insufficient number of breaks in a day, distance of travel, peerpressure and the presence of insufficient infrastructure. This studyclearly shows us that a student balance his personnel interestsand his academics further the college should provide an ideal environment so as to allow the student to accomplish the former.
International Journal of Academic Multidisciplinary Research (IJAMR), 2024
This study investigates the relationships between academic, financial, and social demands and mental fatigue, with a focus on how these relationships might vary according to demographic and contextual factors. Hypotheses 1a and 1b examine the correlation between academic and financial demands, respectively, and mental fatigue. The study demonstrated a strong positive correlation between academic demands and mental fatigue, with a Pearson correlation coefficient of 0.650, indicating that as academic demands increase, so does mental fatigue. This correlation is statistically significant with a p-value of 0.000, confirming the robustness of this relationship. Similarly, Table 2 presents a Pearson correlation coefficient of 0.569 between financial demands and mental fatigue, also statistically significant at the 0.01 level, suggesting that higher financial demands are associated with increased mental fatigue. Further analysis in Table 3, which applies logistic regression, explores the predictive power of academic and financial demands on mental fatigue while controlling for confounders. The results reveal that academic demands significantly predict higher mental fatigue, with a coefficient of 0.65 and an odds ratio of 1.910, while financial demands also significantly predict mental fatigue, with a coefficient of 0.75 and an odds ratio of 2.117. These findings indicate that both academic and financial demands substantially contribute to the likelihood of experiencing mental fatigue. Table 4 expands on these findings by incorporating additional variables such as social demands, age, gender, and study hours into the regression model. It confirms that academic demands (coefficient = 0.40), financial demands (coefficient = 0.35), and social demands (coefficient = 0.30) are significant predictors of mental fatigue, while age and study hours also show statistically significant relationships with mental fatigue. Gender does not significantly affect mental fatigue, highlighting that the impact of academic, financial, and social demands on mental fatigue remains consistent across genders. Table 5 presents a logistic regression analysis that includes all previously discussed variables to further investigate the relationships among academic, financial, and social demands, gender, age, and study hours with mental fatigue. The results show that academic demands (odds ratio = 1.822), financial demands (odds ratio = 1.648), and social demands (odds ratio = 1.419) are significant predictors of mental fatigue, while age (odds ratio = 1.051) and study hours (odds ratio = 1.161) also contribute to the likelihood of mental fatigue. Gender remains a non-significant predictor, underscoring its limited impact on mental fatigue compared to other variables. Universities and educational institutions should establish comprehensive support services to help students manage academic demands. This could include tutoring programs, academic counseling, and stress management workshops designed to alleviate the pressure associated with rigorous academic schedules
Global Journal of Sociology: Current Issues
Scholar fatigue should be carefully evaluated to be able to interfere when needed. Goals: the evaluation of differences/similarities regarding fatigue at pupils studying at different. High schools. Material and methods: the study was done on a group of 237 teenagers studying at 3 different high schools in Iasi: Sport high school (75 teenagers), Music High school (73 students) and Grammar School (89 students). The pupils had to fill in a questionnaire regarding the emersion of fatigue. The results were analysed using the Pearson CHI Square test. Results and discussions: in 45.99% of cases, students are often tired, the calculated differences being statistically significant for a p<0.01 (f=4, χ²=15.500), fatigue being often acknowledged by Grammar School teenagers. The phenomenon appears in the middle of the week (48.10% ,p>0.001 f=4, χ²=20.862) and at midday (43.03%, p<0.01, f=4, χ²=11.738). The statistically significant differences show a high frequency of positive answer...
Cognitive flexibility in healthy students is affected by fatigue: An experimental study
Learning and Individual Differences, 2015
Fatigue is a common problem in healthy individuals, but the effects on cognition are poorly understood. The current experimental study investigated the relation between fatigue and cognitive flexibility. Sixty university students were randomly assigned to an experimental group or a control group. The experimental group received a fatigue-inducing session in which they performed cognitively demanding tasks. The control group received non-demanding tasks. After the intervention, both groups performed a switch task with two task rules of unequal difficulty. Both induced fatigue and fatigue state at baseline were evaluated. Difficulties in task switching, irrespective of task rule, were more pronounced in students in both groups who had higher fatigue at baseline. The experimental group responded slower under all conditions. Moreover, the experimental group took longer to switch from the difficult to the easy task rule compared to the opposite direction. These findings suggest that fatigue negatively affects cognitive flexibility in university students.
The aim of our study was to assess university students’ sleep quality and fatigue before and during the academic exam period and identify potential associated factors. A Web-based survey was completed by 940 students of 20 different Tertiary Institutions including demographics, sleep habits, exercise, caffeine, tobacco, alcohol use, subjective sleep quality (Pittsburgh Sleep Quality Index - PSQI) and fatigue (Fatigue severity scale – FSS) at the beginning of semester and at examination period. During exam period, PSQI (8.9 vs 6.1, p<0.001) and FSS scores (36.9 vs 32.7, p<0.001) were significantly elevated compared to pre-exam period. Increase of PSQI score was associated with age (β=0.111, p=0.011), presence of chronic disease (β=0.914, p=0.006), and depressive symptoms (β=0.459, p=0.001). Increase of FSS score was associated with female gender (β=1.658, p<0.001), age, (β=0.198, p=0.010), increase in smoking (β=1.7, p=0.029), coffee/energy drinks consumption (β=1.988, p<...
Fatigue and senior high school adolescents
2015
Conclusion: Relatively high proportions of fatigue, sleep problems, daytime sleepiness, and depression among senior high school adolescents were found in our study. Read this original research and sign up to receive Neuropsychiatric Disease and Treatment journal here: http://www.dovepress.com/articles.php?article\_id=20933