Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study

Павлова, Юлія; Zikrach, Dmytro; Mosler, Dariusz; Ortenburger, Dorota; Góra, Tomasz; Wąsik, Jacek

doi:10.1371/journal.pone.0239749

Cited by 15 publications

(10 citation statements)

References 48 publications

(64 reference statements)

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“…The study results indicate that future anxiety was not significantly high among international students from war and conflict areas. This result is consistent with the results of a study conducted by Pavlova et al (2020) , which indicates that there is no clear relationship between the level of anxiety and the experience of living in an environment of war and conflict among young people and that these results can be explained that subjective feelings often affect feelings of anxiety. As for some demographic variables and their relationship to future anxiety, the study results showed a statistically significant relationship between demographic variables (economic status and academic year) and the level of future anxiety among students in the study sample.…”

Section: Discussionsupporting

confidence: 91%

Future Anxiety Among Young People Affected by War and Armed Conflict: Indicators for Social Work Practice

Alotaibi

2021

Front. Sociol.

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Strengthening the evidence base for professional social work intervention that contributes to providing psychosocial support to international students affected by war and conflict is a major priority as this vulnerable group of youth increases. Therefore, this study aimed to determine the level of future anxiety among international students coming from areas experiencing war and conflict. This study used the descriptive correlative approach, where the future anxiety scale was applied to a sample of 287 international students affected by war and conflicts. Findings showed that there are statistically significant differences between males and females (in favor of females) in the level of the social dimension of future anxiety. The current study results showed a statistically significant relationship between future anxiety and some variables related to war and conflict (living in a war environment - direct and indirect exposure to damage). There are statistically significant differences between those who lived in Yemen at the time of wars and those who did not live (in favor of those who lived in Yemen at the time of wars) in the level of future anxiety. There are also statistically significant differences between those exposed to harm or their family because of the war and those who were not exposed (in favor of those who were exposed) in the level of future anxiety as a whole. The study recommends developing psychosocial support services for this vulnerable group, considering the cultural context to promote women and protect them from discrimination in the services they deserve on an equal basis with men.

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Section: Discussionsupporting

confidence: 91%

Future Anxiety Among Young People Affected by War and Armed Conflict: Indicators for Social Work Practice

Alotaibi

2021

Front. Sociol.

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“…Besides pandemic, there is ongoing military conflict in Ukraine. It was proved that threat of conscription to army of students could affect their anxiety levels [45]. Therefore, pandemic cannot be perceived as single factor elevating anxiety level in this study.…”

Section: Discussionmentioning

confidence: 64%

Anxiety level in context of chosen pro-health attitudes of male students during COVID-19 pandemic

2021

JOMH

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Background and objective: Assessing the impact of different factors on anxiety level is a complex and challenging problem, especially during pandemic or similar life threatening situations. Stress can affect dietary and eating behaviors. The aim of this study is to extend knowledge concerning the relation between increased anxiety level during pandemic and attitude towards dietary and eating behaviors in the context of social situation and support of relatives and friends. This study was conducted in 2020 during COVID-19 pandemic. Methods: State-Trait Anxiety Inventory alongside auxiliary questions about social relations and eating habits were asked to the male students from different universities and courses in Eastern Europe. To assess differences and dynamics of anxiety level, multiple regression models were used. Results: Multiple regression models between state anxiety level with context to the following factors: paying particular attention to one's diet, namely, the way of nutrition during severe anxiety felt during the pandemic and factors such as strong social support and type of university course was R = 0.41 (p = 0.00). For trait-anxiety the same model returned R = 0.39 (p = 0.00). Analysis of variance revealed that support of relatives is significant factor for state anxiety level, whereas this factor is insignificant for trait anxiety level. Conclusions: Models obtained from this study indicated that there are significant relations between anxiety level of male students and social support, which is expressed in the form of proper eating, therefore pro-health habits are revealed during elevated prolonged stress state such as COVID-19 pandemic.

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“…It can be seen that Source: Authors all of the input variables have similarities to the normal or uniform distributions. This is a good quality, which is commonly wanted within datasets used in machine learning applications [21]. The output is distributed exponentially, as shown in Figure 2.…”

Section: Datasetmentioning

confidence: 96%

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Šegota

Lorencin

Šercer³

et al. 2021

Pomorstvo (Online)

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Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression (SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.

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Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study

Cited by 15 publications

References 48 publications

Future Anxiety Among Young People Affected by War and Armed Conflict: Indicators for Social Work Practice

Future Anxiety Among Young People Affected by War and Armed Conflict: Indicators for Social Work Practice

Anxiety level in context of chosen pro-health attitudes of male students during COVID-19 pandemic

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

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