Exploratory Analysis Research Articles

Seeing the Trees: How a Concrete versus Abstract Mindset Improves Performance on Low-Level Assurance Tasks

ABSTRACT Prior research shows that individuals benefit from abstract construals when performing broader high-level assurance tasks. We extend this research by studying the effects of different construal mindsets on skepticism and performance in a low-level assurance task setting. Using a sample of 195 online participants, we find that individuals benefit from concrete (versus abstract) mindsets when performing low-level assurance tasks because concrete construals better allow them to focus on detailed evidence that may contradict client assertions. Specifically, we show that assurance providers who utilize a concrete (abstract) construal mindset when performing a low-level task display better (worse) skeptical judgments and actions. Taken together with findings from past research, our results support the notion that assurance providers should utilize the construal mindset that best matches the characteristics of the task at hand. Exploratory analysis suggests that client retention incentives may moderate the relationship between skeptical judgment and skeptical action in our context. Data Availability: Data are available from the authors upon request.

Negative cosmological constant in the dark energy sector: tests from JWST photometric and spectroscopic observations of high-redshift galaxies

Early observations with the James Webb Space Telescope (JWST) have revealed the existence of an unexpectedly large abundance of extremely massive galaxies at redshifts z ≳ 5: these are in tension with the predictions not only of the standard ΛCDM cosmology, but also with those of a wide class of dynamical dark energy (DE) models, and are generally in better agreement with models characterized by a phantom behaviour. Here we consider a model, inspired by string theory and the ubiquity of anti-de Sitter vacua therein, featuring an evolving DE component with positive energy density on top of a negative cosmological constant, argued in an earlier exploratory analysis to potentially be able to explain the JWST observations. We perform a robust comparison of this model against JWST data, considering both photometric observations from the CEERS program, and spectroscopic observations from the FRESCO survey. We show that the model is able to accommodate the JWST observations, with a consistency probability of up to 98%, even in the presence of an evolving component with a quintessence-like behaviour (easier to accommodate theoretically compared to phantom DE), while remaining consistent with standard low-redshift probes. Our results showcase the potential of measurements of high-redshift galaxy abundances in tests of fundamental physics, and their complementarity with standard cosmological probes.

Quality of life and patient-reported toxicities in patients with advanced Merkel cell carcinoma treated with combined nivolumab and ipilimumab with or without stereotactic body radiation therapy.

Merkel cell carcinoma is a rare skin cancer associated with poor survival. Based on a previous Phase II trial of adults with advanced Merkel cell carcinoma by Kim and colleagues (2022), there is now a strong rationale for combination therapy (i.e., nivolumab and ipilimumab) to become a treatment option for patients with advanced Merkel cell carcinoma. The goal of this paper was to report on the secondary outcome of quality of life (QOL) among patients on this trial. Patients receiving combined nivolumab and ipilimumab, with or without stereotactic body radiation therapy (SBRT), completed the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 prior to starting treatment and every 2 weeks thereafter. Changes in QOL during treatment and post-treatment were evaluated using piecewise random-effects mixed models. Exploratory analyses compared changes in QOL between study arms. The original trial was registered with ClinicalTrials.gov (NCT03071406). Study participants (n = 50) reported no changes in overall QOL (ps > 0.05), but emotional functioning improved during treatment (p = 0.01). Cognitive and social functioning worsened post-treatment (ps < 0.01). In general, patients treated with combination therapy only (n = 25) reported no change in QOL over time, whereas patients also treated with SBRT (n = 25) consistently demonstrated worsening QOL post-treatment. QOL is generally preserved in patients treated with combination therapy, but the addition of SBRT may worsen QOL. Combined with clinical efficacy data published previously, results support the use of combination therapy with nivolumab and ipilimumab as a treatment option for patients with advanced Merkel cell carcinoma.

HAP-FAST: a feasibility study incorporating qualitative, mechanistic and costing sub-studies alongside a randomised pilot trial comparing chest x-ray to low-dose CT scan and empirical antibiotics to antibiotics guided by the BIOFIRE® FILM ARRAY® pneumonia plus panel in adults with suspected non-ventilator-associated hospital-acquired pneumonia

IntroductionNon-ventilator-associated hospital-acquired pneumonia (nv-HAP) is the most common healthcare-associated infection (HCAI), is associated with high mortality and morbidity and places a major burden on healthcare systems. Diagnosis currently relies on...

Authentication of Mentha arvensis essential oil using attenuated total reflectance-Fourier transform infrared spectrophotometry coupled with chemometrics

Mentha arvensis essential oil is considered a product of high economic value that finds application in the food, pharmaceutical, and cosmetic industries. They are subjected to adulteration with synthetic chemicals and low-cost vegetable oils with higher boiling points. A rapid, non-destructive method using infrared spectroscopy has been investigated to authenticate Mentha arvensis Essential oils, helping to maintain their quality and trustworthiness for users who rely on them for their beneficial properties. Authentic samples of Mentha arvensis essential oil were obtained and analyzed using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy in the wavenumber range of 500–4000 cm−1. The resulting spectra were pre-processed with Multiplicative Scatter Correction. The initial investigation involved exploratory analysis using Principal Component Analysis, followed by discriminant analysis using Principal Component Analysis-Linear Discriminant Analysis, Partial Least Squares-Discriminant Analysis, and Soft Independent Modelling for Class Analogy. The classification models developed have a high accuracy of 100 % thus demonstrating higher efficiency in distinguishing different adulterants in Mentha arvensis essential oil with both high specificity and sensitivity. To assess the extent of adulteration in Mentha arvensis essential oil, Partial Least Squares Regression and Principal Component Regression were employed. The regression models were capable of identifying adulteration levels as low as 5 % in the essential oil thus enabling FTIR spectroscopy coupled with chemometrics to serve as a non-destructive method for identifying commonly used adulterants, potentially replacing traditionally used Chromatographic and Thermo-Gravimetric Analysis methods.

A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1 % of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6 % average decrease in RMSE and an average increase of 1 % in R2) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Transcriptome Analysis on the ALAN-Induced Zebrafish Ovary: Desynchronization of Life Processes and Initiation of Diseases

Objective: The effect of artificial light at night (ALAN) on “transcriptome” is prominent owing to its capacity for “desynchronization” of organismal physiology. Light influences the circadian rhythm. This study aims to explore the ALAN-induced ovarian transcriptome of zebrafish for desynchronization of life processes. Methods: Four experimental conditions were set up for female zebrafish: one normal 12-hour light and 12-hour dark (LD) cycle, and three continuous exposures to ALAN for one week (LLW), one month (LLM), and one year (LLY). The whole transcriptome data analysis of the ALAN-exposed samples was then compared with the normal sample using RNA-Seq, followed by exploratory analyses. Results: The analysis revealed two different patterns of expression of genes where LLW and LLM differ with LLY samples in comparison to LD. Compared to LD, downregulation of the predicted hub genes was observed in all treatments; ribosome and oxidative phosphorylation pathways were enriched. LLY vs. LD contrast depicts the enrichment of three more pathways—RNA polymerase, adhesion junction, and signaling. The gene ontology (GO) enrichment portrays more prevalent biological processes in LLW vs. LD and LLM vs. LD than in LLY vs. LD. Contrast-wise disease annotation represents neoplasms as the most prevalent; disease enrichment denotes the major class of neoplasm, carcinoma, coupled with intellectual disability, global developmental delay, and seizures. Conclusion: Our study displayed desynchronization of various genes and pathways leading to the initiation of diseases, for the first time in zebrafish. Even though, this data shows that ALAN is a serious threat, further research is needed to determine the intensity and the duration of ALAN which might cause potential repercussions.

HRD Conducts Research Using Public Sector Banks and Private Sector Banks

The banking sector, which is reliant on the health of the public, was discussed in this article. Only people use its services, and they are also the only ones who directly influence productivity. Up until 1969, banking was still in some ways a traditional enterprise. when the Indian government took over major banks. Banks became a powerful force for social change after being nationalized and ceased to be the sole source of finance for industry. The assertion that the banking industry is increasing astronomically is supported by additional proof in the form of statistics. The Indian banking industry needs to be governed by banking professionals if it is to profit from the sector on a global scale. The goal of the study is to look at four important factors that have a big impact on how the banking industry develops in private sector banks: selection and recruitment policies, training, performance appraisal, and compensation. For this descriptive and exploratory analysis, the researcher used a standard random sampling technique. In the study, employee impressions of the HRD environment in the two largest banks are assessed. Additionally, it looks to examine if bank employees working for public and private sector banks have different perspectives on each facet of the HRD climate. The HRD climate is divided into three categories: General Climate, OCTAPACE, and HRD Mechanisms. 200 employees submitted the primary data using a standardized questionnaire with 40 items. KEY WORDS Performance review, bank employees, private banks, and methods of human resource management.

LIVESTOCK FARMERS RISK PERCEPTION AND WILLINGNESS TO PAY FOR LIVESTOCK INSURANCE IN FLOOD-PRONE AREAS OF PUNJAB

Pakistan is the world’s most susceptible country to extreme climatic events, such as floods. As such, for many years, the country has faced the risk of losses from natural calamities without crop insurance. However, in recent years, the national government has undertaken crop insurance initiatives in Pakistan. Consequently, there is limited knowledge available on the adoption of crop insurance in Pakistan. To fill this gap, this study aims to examine the factors influencing the willingness to pay for livestock insurance in flood prone areas of Punjab. Primary data were collected from 204 farm households through a structured interview in flood-prone areas of Punjab. Data was analyzed with descriptive statistics and logistics models. The explanatory analysis was used to further look at the socio-economic characteristics of livestock farmers through MS Excel and binary logistics models to test the willingness to pay livestock farmers for insurance. The results show a positive relationship between the perceived risk of Farmers and the willingness to pay for insurance programs. The findings of this study show that the probability of farmers' WTP is increased due to a better understanding of financial institutions and having more technical knowledge. The study results show that farmers with adult males are more likely to pay livestock insurance.

An Exploratory Analysis of the Association between Healthcare Associated Infections & Hospital Financial Performance.

Purpose: Healthcare-associated infections (HAIs) place a significant financial burden on United States hospitals. HAI treatments extend hospital lengths of stay and increase hospital operational costs while significantly reducing hospital profit margins. Given these challenges, the research aim of this study was to explore the association between HAIs and hospital financial performance. A better understanding of this relationship can assist hospital leaders in optimizing the use of scarce financial resources to reduce HAI prevalence. Methods: Data for calendar year 2022 for active short-term acute care hospitals (n = 1454) in the US were analyzed using multiple linear regression analysis. We explored two derived dependent variables, operating expense per staffed bed and operating expense per discharge. The independent variables included four healthcare-associated infection rates: methicillin-resistant Staphylococcus aureus (MRSA) infection rate, Clostridium difficile (C. diff) infection rate, Catheter-Associated Urinary Tract Infection (CAUTI) rate, and Central Line Associated Blood Stream Infections (CLABSI). Appropriate organizational and market-level variables that may independently influence hospital financial performance were included as control variables. Results: The results revealed that C. diff (β: 0.037, p < 0.05) and CAUTI (β: 0.031, p < 0.05) rates were positively associated with an increase in operating expense per staffed bed, while increases in MRSA (β: 0.042, p < 0.001), C. diff (β: 0.062, p < 0.001), and CAUTI rates (β: 0.039, p < 0.001) were correlated with increased operating expenses per discharge. Conclusions: This study demonstrates that specific HAIs may be associated with increased hospital expenses. Proactively targeting these infections through tailored interventions may lead to reduced hospital costs, improved financial performance, and economic stability.

Unravelling the Effects of Climate Change on Agriculture of Pakistan: An Exploratory Analysis

The agricultural sector of Pakistan, the backbone of the economy, has experienced extreme changes in climate over the past years. Varying temperatures, rainfall patterns, and carbon emissions have drastically affected the country's agricultural productivity. This study focuses on the exploratory analysis of various factors of climate change in the agricultural sector of Pakistan and involves a descriptive analysis of three variables: temperature, rainfall, and carbon emissions. Time series data on monthly temperature and rainfall was taken from the World Bank from the year 1990 to 2018. At the same time, carbon emissions were studied on a yearly basis. The analysis showed that the country had experienced an increase in temperature and carbon emissions during the period of analysis. However, the decreasing trends of rainfall during most of the months explored the deteriorating agricultural production and food insecurity in the country. Therefore, the government should employ innovative techniques to harvest rainwater for agricultural development, aiming to reduce carbon emissions.

Palestine in a Vicious Circle of Violence, Wars and Terror (1948–2023)

This article deals with one of the most explosive areas of the world today, which is the Near and Middle East (NMEA) and especially the territory of historical Palestine, where the Israeli-Palestinian conflict (IPC) has been going on for a long time. The text deals primarily with historical and military factors and is written as a historical explanatory analysis. It focuses on the most significant historical events and explains the ways of functioning and the consequences of the processes that have been taking place there for a long time.

Data Analysis and Prediction of Of Various app

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Exploratory Data Analysis and Prediction of Rating Of Various app on Google Play Store

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.

The university bundle: Unpacking the sources of undergraduate moral socialization

The university bundle: Unpacking the sources of undergraduate moral socialization

Time-to-Treatment Delays and Their Prognostic Implications in Pharyngeal Cancer-An Exploratory Analysis in Western Romania.

Diagnosis and treatment for pharyngeal cancer are decisive in determining prognosis. Diagnosis delays are frequent, representing a significant cause of avoidable mortality, and an important factor in subpar survival across the continuous HNC care delivery. The present study represents a retrospective analysis of medical records from Western Romania, which included 180 patients, to evaluate the impact of time-to-treatment delay on patients diagnosed with pharyngeal cancer. The data analyses were performed using the Kaplan-Meier method R (version 3.6.3) packages, including tidyverse, final-fit, mcgv, survival, stringdist, janitor, and Hmisc. The mean days from diagnosis until the end of treatment were higher for the nasopharynx group. Cox regression analysis regarding diagnosis to treatment duration categories showed an increased risk mortality by 3.11 times (95%CI: 1.51-6.41, p = 0.0021) with a Harrell's C-index of 0.638 (95%CI: 0.552-0.723). The hypopharynx and oropharynx locations increased risk mortality by 4.59 (95%CI: 1.55-13.55) and 5.49 times (95%CI: 1.79-16.81) compared to the nasopharynx location. The findings of this study led to the conclusion that it seems there is a trend of mortality risk for oropharynx and hypopharynx cancers due to delays in the time to treatment over 70 days, standing as a basis for further research as there is an imperative need for prospective multicenter studies.

Unveiling The Legacy of Siti Walidah (1872-1946): Exploring the Education, Life, And Works of Indonesian Female Ulama

This study delves into the da'wah contributions of the Indonesian female ulama, Siti Walidah (1872-1946). Employing qualitative methods, the research entails creating and observing various works, books, academic research, videos, internet sources, and historical documents. The gathered data undergoes analysis using the explanatory analysis method, where the logic of social phenomena aligns with common sense through historical, anthropological, and sociological approaches. The research aims to exemplify the role model provided by female ulama such as Siti Walidah, allowing contemporary women to learn from and emulate the activities and struggles of these female scholars, thereby yielding numerous benefits for the advancement of the ummah, particularly women. The study presents the concepts of women scholars, their contributions, and the strategies they employed. Additionally, it delves into the supporting and inhibiting factors faced by female ulama in their preaching endeavors, aiming for a comprehensive understanding of their experiences.

Translation and validation of the French version of the child perceptions questionnaire for children aged 11 to 14 years old (CPQ11-14) short-form.

The COHQoL is a set of questionnaires used to evaluate the impact of oral health on children's quality of life. Although the CPQ8-10 and the P-CPQ have been translated and validated in French, the CPQ11-14 14 has not yet been validated. The aim was to develop a French version of the CPQ11-14 16-items. The French version of CPQ11-14 was obtained by a forward-backward translation process and pretested. The final version was tested on children aged 11-14 and divided into three groups: children with orofacial clefts, children with rare dental diseases other than clefts, and children without anomalies. We conducted a cross-sectional study and evaluated the reliability with test-retest and internal consistency, and the questionnaire validity with construct validity and discriminant validity. We performed an Exploratory Factory Analysis (EFA). 187 children tested the questionnaire. The ICC of the test-retest was 0.76 and the Cronbach's alpha was 0.77. The correlation between the CPQ11-14 and self-assessment of oral health and general well-being was > 0.2. Patients with orofacial clefts and rare diseases had significantly higher scores for overall short-form CPQ11-14. The EFA revealed six factors. The French CPQ11-14 is valid to assess the impact of oral health on children's quality of life. The translation of this questionnaire into French will enable us to assess the impact of oral health on the quality of life of adolescents. This questionnaire complements the 8-10 years version of the CPQ, as well as the parental version that can be used in conjunction with the questionnaire.

Health and school difficulties of children in foster care in the experience of their foster parents

The period of compulsory school attendance is a mentally very challenging period for children in foster care, which can be impacted by complex developmental trauma stemming from the child's experience in foster care with cumulative and chronic traumatic events in his/her biological family. These events have taken place over a long period of time in interpersonal relationships usually related to caregivers, in the form of abandonment of the child by the caregiver, changing caregivers, lack of safe space and necessary support for the child from the family and school, and/or deficiencies in the provision of coordinated health and social care for both the child and the foster parents. The paper presents research findings supported by a statistical analysis of health and school difficulties in children in foster care related to their school attendance. The purpose of the research was to support the quality and coordination of services of non-profit organizations providing social, health and educational services to foster families and their children during school attendance to support the school success and well-being of children. The study was implemented as part of a dissertation at the University of South Bohemia in České Budějovice in the Czech Republic on the topic of promotion of the health, social and psychological well-being of school-aged children in the context of their school and family environment. The authors carried out a questionnaire survey in the Czech Republic among a selected sample of respondents, who were foster parents of children aged 6 to 15 attending primary school in the Pardubice Region. In a total sample of 102 respondents, we identified two different groups. One group – 64 respondents with healthy children and another group – 38 respondents with children with health difficulties. The aim of the research was to compare the assessments of the intensity of school difficulties of children in foster care in groups of respondents with or without a child with health difficulties or mental illness in their foster care. The hypothesis of a difference in assessments of school difficulties in children with/without health difficulties was formulated and statistically tested (H0: ratings of the intensity of difficulties are the same in both groups). Statistical testing of the hypothesis utilised the non-parametric Mann-Whitney test with the test results being significant at the level of significance p&lt;0.05. Correlation was observed for each difficulty and exploratory cluster analysis shows their interrelationships. The study results according to the foster parents' assessment show a high intensity of difficulties of school-aged children in foster care in their inclusion in the classroom in establishing relationships with classmates (p-value =0.9), in a negative attitude towards learning and lack of motivation to learn (p-value =0.7) experiencing nervousness, internal tension and staying focused when learning. In addition, when comparing the groups of respondents with healthy children and children who have health difficulties/mental illness, there was a statistically significantly higher intensity of difficulties in the group of respondents with children with health difficulties when doing prep work for the classes (p-value =0.00075).