Prevalence and Predictors of Anxiety in Healthcare Professions Students

Macauley, Kelly; Plummer, Laura; Bemis, Charlotte; Brock, Genevieve; Larson, Christine L.; Spangler, Johanna

doi:10.1016/j.hpe.2018.01.001

Cited by 62 publications

(82 citation statements)

References 46 publications

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“…Another study highlighted that females were twice as likely to be affected by anxiety disorders. What has been observed could be explained by another study that the underlying hormonal and biochemical differences between men and women could influence mental health [ 41 ]. In addition, the results of this study showed that the students’ academic year was significantly associated with stress.…”

Section: Discussionmentioning

confidence: 99%

Stress, Anxiety and Depression among a Cohort of Health Sciences Undergraduate Students: The Prevalence and Risk Factors

Fauzi

Anuar

Kek

et al. 2021

IJERPH

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Stress, anxiety, and depression (SAD) have a negative impact on the learning and academic performance of university students. Hence, this study aimed to determine the prevalence, as well as the risk factors associated with SAD among a cohort of students pursuing undergraduate degree courses in health sciences. This is part of the strategy in building a healthy nation. A questionnaire containing socio-demographic factors and the short version of Depression, Anxiety, and Stress Scale-21 (DASS-21) was used to assess the likelihood of psychological distress. Logistic regression analysis was conducted to determine the risk factors of SAD. In total, 449 students completed the questionnaire (93.9% response rate). Of these, 65% had stress, 85.1% had anxiety and 51.4% had depression. Most cases of stress (74.6%) and depression (66.2%) were of normal-to-mild level, while 74.6% of them showed moderate-to-extremely severe anxiety. There was a statistically significant association between stress score and the year of study. In the regression analysis, poor sleep quality and fatigue were risk factors of anxiety and depression, whereas low-grade fever and frequent headaches were risk factors for stress and anxiety. Stress, anxiety, and depression scores were significantly higher among students studying medical imaging. A substantial proportion of health science students are suffering from SAD. This study recommends screening and close monitoring of the above-mentioned predictors and the formulation of comprehensive intervention strategies for students with SAD.

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Section: Discussionmentioning

confidence: 99%

Stress, Anxiety and Depression among a Cohort of Health Sciences Undergraduate Students: The Prevalence and Risk Factors

Fauzi

Anuar

Kek

et al. 2021

IJERPH

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show abstract

“…The scale presents an internal consistency of 0.94 for state anxiety and 0.90 for trait anxiety [ 27 ]. It has been applied in several previous studies, such as the study carried out by Macauley et al [ 28 ] in a group of 183 students to measure their anxiety levels in relation to professions related to the health field. The Cronbach’s alpha coefficient for the current sample for the state anxiety subscale was 0.93, while for the trait anxiety subscale, it was 0.89.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Anxiety, Suicidal Risk, Daily Stress, Empathy, Perceived Emotional Intelligence, and Coping Strategies in a Sample of Spanish Undergraduates

Montero

Morales-Rodríguez

2021

IJERPH

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Anxiety disorders and suicide are commonly under-recognized issues that can be a public health problem. Adolescents are among the most affected population groups and studying them can prevent serious health problems. These two elements are related, but at the same time, they can only be understood from a multifactorial point of view, so other related variables such as emotional intelligence, empathy, or coping strategies are key to understand their effect on the population. In this study, a series of methods to measure the variables of interest were applied to a specific group of adolescents to determine their mental health levels, focusing on suicide and anxiety episodes. The results reflected average levels with a tendency to be high in the case of anxiety and nonalarming levels in terms of suicide risk, both parameters presenting more worrying values in women. In turn, the correlation between suicide and anxiety was demonstrated considering the other variables (coping strategies, empathy, emotional intelligence, and prosociality). This research has relevant implications for the diagnosis, orientation, and design of psychoeducational and clinical interventions that contribute to the improvement of their well-being and quality of life.

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“…The challenges encountered using regression methods are that data must be quantitative, normalized, no extreme data and no multicollinearity. Data with extreme values and attributes used in relation to one another leads to improper use of estimates [36], [37]. The amount of data sampling used in the data mining method must be large so that the generalization of the population can be created as in [38]- [41].…”

Section: Predictive Models Used In Previous Studies and Resultsmentioning

confidence: 99%

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

2019

ijrte

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In Malaysia, the students’ academic assessment has changed over time as it is now made to be more holistic. Predictive models used for educational data in Malaysia are still inadequate in giving a true picture of the students’ academic performance. The reason behind this is due to the lack of research in Malaysian context on predictive models and factors affecting students' academic achievement. Therefore, this study will conduct a systematic review on the literature, in order to identify the predictive methods for students’ academic performance in higher education. Through three major bases: WoS, Science Direct and IEEE Xplore, an extensive search was conducted to find any related articles from 2014 to 2018 that use "predict", "forecast", "academic performance", "student" and "higher education" keywords in their text. Initially, 195 articles were selected to be used in this review. However, through titles and abstracts’ filtering and screen processing, only 69 articles found to discuss on the predictive model for student's academic performance at higher education level. Based on extensive reading, the most widely used attributes in the predictive models is the academic process. While predictive models can be categorized into three, namely classification, cluster and regression, there are nine methods used by previous researchers in all categories. The most widely used category from previous studies was the classification, with 33 articles. This study has listed the advantages and disadvantages of each method based on the previous studies. This study also has identified the challenges and gaps faced by previous researchers in improving the existing models in the future. Among the challenges faced by the predictive models are the amount of data and assumptions that need to be followed before analysis can be made. In the future, the predictive models used for students’ academic performance should consider the latest assessments’ valuation method based on a modern educational system that emphasizes on soft skills, interpersonal skills and high-level thinking capabilities.

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Prevalence and Predictors of Anxiety in Healthcare Professions Students

Cited by 62 publications

References 46 publications

Stress, Anxiety and Depression among a Cohort of Health Sciences Undergraduate Students: The Prevalence and Risk Factors

Stress, Anxiety and Depression among a Cohort of Health Sciences Undergraduate Students: The Prevalence and Risk Factors

Evaluation of Anxiety, Suicidal Risk, Daily Stress, Empathy, Perceived Emotional Intelligence, and Coping Strategies in a Sample of Spanish Undergraduates

Systematic Research on Predictive Models on Students’ Academic Performance in Higher Education

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