This study focused on the relationship between emerging didactical strategies and delivery of quality education service to students with learning disabilities in Cross River State, Nigeria. It adopted quantitative methodology and descriptive survey as the design. The population consist of all stakeholders with bias in learning disabilities in the state with 61 participants purposively sampled for the study, two null hypotheses were formulated to guide the study. A 20 item self-developed and validated questionnaire of 4 points Likert scale titled; Delivery of quality Education Service (QDES) was used for data collection. Data were statistically analyzed using Pearson Product Moment Cor- relation Analysis at 0.05 level of significance with assistance of SPSS software. Findings indicate strong positive relationship between the variables, this means that, ICT-tools and instructional accommodations are essential to the provision of quality education service to students with learning disabilities. It was recommended among others that, ICT-tools and instructional accommodations should compulsorily be an integral part of educational plan for these learners, the capacity of teachers and learners should be upgraded to meet the emerging realities of the 21st century education system and finally, policy and legislative frame work should support practical use of these strategies to improve and sustain quality service delivery in schools

BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

• MeSH
• dospělí MeSH
• internet MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• neuromuskulární nemoci * diagnóza   diagnostické zobrazování   MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Aim: Research on the health consequences of violent victimization of people with disabilities is lacking. This study aims to identify the factors that are associated with physical and mental health impacts of anti-disability bias victimization. Methods: The study drew on a unique sample of 331 self-identified people with disabilities, all over the age of 15, residing in Czechia. From this sample, 47 questionnaires were excluded. The respondents were asked about the most serious incident of anti-disability bias victimization in the last five years. A series of bivariate binary logistic regressions were performed - with the consequences of this incident as outcomes (mental health and physical health). Results: 90 respondents (32%) reported experiencing the most serious incident of bias victimization in the last five years. 60% of victims reported anxiety and sadness, and 28% deterioration in physical health. The results suggest that victims experience physical and mental health consequences unequally. Age, perceived disability in specific areas, visibility of disability, presence of multiple disabilities, and number of offenders are associated with the experience of physical health deterioration. Education, perceived disability in specific areas, and visibility of disability are associated with the experience of mental health impacts. Conclusion: Certain groups of people with disabilities who experience victimization report poorer physical and mental health outcomes. This differential experience should be considered in immediate responses and prevention programs.

• MeSH
• duševní zdraví statistika a číselné údaje   MeSH
• emoce MeSH
• lidé MeSH
• logistické modely MeSH
• násilí psychologie   statistika a číselné údaje   MeSH
• postižení * psychologie   statistika a číselné údaje   MeSH
• předsudek * psychologie   statistika a číselné údaje   MeSH
• sociální problémy psychologie   statistika a číselné údaje   MeSH
• zdraví statistika a číselné údaje   MeSH
• zpráva o sobě MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• práce podpořená grantem MeSH

Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models.

• Publikační typ
• časopisecké články MeSH

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual's autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring.

• Publikační typ
• časopisecké články MeSH

OBJECTIVES: The aim of the study was to evaluate widespread dietary supplements (DSs) use among the military population. There is no recent study to comprehensively evaluate the prevalence of DS use among the military population. Therefore, this systematic review and meta-analysis aimed to present an overview and estimate of the overall prevalence of DSs use among the military population. METHODS: PubMed, Scopus, Web of Science, and Google Scholar databases were searched up to September 2023 using relevant keywords. All original articles written in English evaluating the prevalence of DSs use among the military population were eligible for this study. The risk of bias assessment of the included studies was done using the Joanna Briggs Institute critical appraisal checklist. The meta-analysis was performed utilizing a random-effects model and STATA software. RESULTS: In total, 32 cross-sectional studies were included in this review. The prevalence rate of DS use in the overall military population was 57% (95% CI: 49-64); this rate was higher in the studies that were carried out in the USA and the studies with a sample size lower than 10,000 members. Eleven studies reported adverse effects (AEs) following DSs use in the military population, the pooled effect size of them was 13.0% (95% CI: 6-20). The most common AEs reported by military personnel were abdominal pain, nausea, vomiting, and diarrhoea, however, they did not include any serious complications. CONCLUSION: The findings indicate that the prevalence of DSs use among the military personnel was high. Moreover, some studies reported AEs following DSs use such as gastrointestinal symptoms. Promotion of knowledge and informed attitudes regarding the DSs use in the military population could be useful.

• MeSH
• lidé MeSH
• ozbrojené síly * statistika a číselné údaje   MeSH
• potravní doplňky * statistika a číselné údaje   škodlivé účinky   MeSH
• prevalence MeSH
• průřezové studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH

OBJECTIVES: To assess the ability, as well as factors affecting the ability, of ultrasound examiners with different levels of ultrasound experience to detect correctly infiltration of ovarian cancer in predefined anatomical locations, and to evaluate the inter-rater agreement regarding the presence or absence of cancer infiltration, using preacquired ultrasound videoclips obtained in a selected patient sample with a high prevalence of cancer spread. METHODS: This study forms part of the Imaging Study in Advanced ovArian Cancer multicenter observational study (NCT03808792). Ultrasound videoclips showing assessment of infiltration of ovarian cancer were obtained by the principal investigator (an ultrasound expert, who did not participate in rating) at 19 predefined anatomical sites in the abdomen and pelvis, including five sites that, if infiltrated, would indicate tumor non-resectability. For each site, there were 10 videoclips showing cancer infiltration and 10 showing no cancer infiltration. The reference standard was either findings at surgery with histological confirmation or response to chemotherapy. For statistical analysis, the 19 sites were grouped into four anatomical regions: pelvis, middle abdomen, upper abdomen and lymph nodes. The videoclips were assessed by raters comprising both senior gynecologists (mainly self-trained expert ultrasound examiners who perform preoperative ultrasound assessment of ovarian cancer spread almost daily) and gynecologists who had undergone a minimum of 6 months' supervised training in the preoperative ultrasound assessment of ovarian cancer spread in a gynecological oncology center. The raters were classified as highly experienced or less experienced based on annual individual caseload and the number of years that they had been performing ultrasound evaluation of ovarian cancer spread. Raters were aware that for each site there would be 10 videoclips with and 10 without cancer infiltration. Each rater independently classified every videoclip as showing or not showing cancer infiltration and rated the image quality (on a scale from 0 to 10) and their diagnostic confidence (on a scale from 0 to 10). A generalized linear mixed model with random effects was used to estimate which factors (including level of experience, image quality, diagnostic confidence and anatomical region) affected the likelihood of a correct classification of cancer infiltration. We assessed the observed percentage of videoclips classified correctly, the expected percentage of videoclips classified correctly based on the generalized linear mixed model and inter-rater agreement (reliability) in classifying anatomical sites as being infiltrated by cancer. RESULTS: Twenty-five raters participated in the study, of whom 13 were highly experienced and 12 were less experienced. The observed percentage of correct classification of cancer infiltration ranged from 70% to 100% depending on rater and anatomical site, and the median percentage of correct classification for the 25 raters ranged from 90% to 100%. The probability of correct classification of all 380 videoclips ranged from 0.956 to 0.975 and was not affected by the rater's level of ultrasound experience. The likelihood of correct classification increased with increased image quality and diagnostic confidence and was affected by anatomical region. It was highest for sites in the pelvis, second highest for those in the middle abdomen, third highest for lymph nodes and lowest for sites in the upper abdomen. The inter-rater agreement of all 25 raters regarding the presence of cancer infiltration ranged from substantial (Fleiss kappa, 0.68 (95% CI, 0.66-0.71)) to very good (Fleiss kappa, 0.99 (95% CI, 0.97-1.00)) depending on the anatomical site. It was lowest for sites in the upper abdomen (Fleiss kappa, 0.68 (95% CI, 0.66-0.71) to 0.97 (95% CI, 0.94-0.99)) and highest for sites in the pelvis (Fleiss kappa, 0.94 (95% CI, 0.92-0.97) to 0.99 (95% CI, 0.97-1.00)). CONCLUSIONS: Ultrasound examiners with different levels of ultrasound experience can classify correctly predefined anatomical sites as being infiltrated or not infiltrated by ovarian cancer based on video recordings obtained by an experienced ultrasound examiner, and the inter-rater agreement is substantial. The likelihood of correct classification as well as the inter-rater agreement is highest for sites in the pelvis and lowest for sites in the upper abdomen. However, owing to the study design, our results regarding diagnostic accuracy and inter-rater agreement are likely to be overoptimistic. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

• MeSH
• audiovizuální záznam MeSH
• břicho diagnostické zobrazování   patologie   MeSH
• invazivní růst nádoru diagnostické zobrazování   MeSH
• klinické kompetence * MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory vaječníků * diagnostické zobrazování   patologie   MeSH
• odchylka pozorovatele MeSH
• pánev diagnostické zobrazování   patologie   MeSH
• prevalence MeSH
• senioři MeSH
• ultrasonografie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• pozorovací studie MeSH
• práce podpořená grantem MeSH

BACKGROUND AND OBJECTIVES: Individuals with acrophobia (fear of heights) can experience severe anxiety or panic attacks when they are located at height. This randomized controlled study aimed to verify the effects of a novel scalable virtual reality-based exposure (VR exposure) tool in individuals with acrophobia, by exposing them to a predefined set of situations they usually tend to avoid. METHODS: Forty-three adults were randomly assigned to one of the two groups: the experimental group or the waitlist group. Both groups attended initial short online education. The experimental group consecutively attended three VR-based exposure therapy (VRET) intervention sessions over 3-5 weeks during which the therapist encouraged participants to enter the predefined feared situations, while the control group on the waitlist had no additional intervention. RESULTS: The findings show that a 3-session VR exposure intervention with a standardized set of tasks effectively reduces the level of experienced height intolerance and particularly avoidance behavior compared to the control waitlist group limited to psychoeducation only. Results were maintained at the 2 months follow-up. The higher the sense of presence after the VR exposure was, the lower the avoidance level rated in the follow-up. LIMITATIONS: Our study has some limitations, such as potential sample selection bias and tracking of only medium-term effects in the 2-month follow-up. CONCLUSIONS: The findings show that three sessions of VR exposure intervention with a standardized set of VR-based scenarios are effective in reducing the level of height intolerance and associated avoidance behavior and led to improvement of the outcome measures two months after the procedure. The role of presence was implicated in the prolonged outcome of the VR exposure intervention.

• MeSH
• dospělí MeSH
• fobie * terapie   psychologie   MeSH
• implozivní terapie * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• následné studie MeSH
• terapie pomocí virtuální reality * metody   MeSH
• virtuální realita * MeSH
• výsledek terapie MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• randomizované kontrolované studie MeSH

Background: The related literature mentions that nurses experience various career problems in their career processes. Some of the problems are related to gender, and glass ceiling perception has an essential place among these. Objective: This study aimed to determine the problems experienced by nurses in their career processes and the effect of glass ceiling perception on these problems. Methods: The population of this descriptive study, conducted between May and June 2022, consisted of 3,758 nurses working in public, private, and university hospitals. The sample consisted of 407 nurses who were randomly selected. Data were collected using the Descriptive Information Form, Glass Ceiling Perception, and Career Problems in Nursing Scale. Normality tests, reliability analyses, descriptive statistical methods, comparison and correlation analyses, and simple linear regression analysis were used to analyse the data. Results: The mean score of the Career Problems in Nursing scale of the nurses participating in the study was 84.75 ± 28.27, and the mean score of the glass ceiling perception scale was 2.80 ± 0.54, above the average. The model established between career problems in nursing and glass ceiling perception was significant and explained 20.3% of the total variance (F = 46.453; p = 0.000; R2adj = 0.203). Conclusion: This study found that the career problems of nurses were above average, and glass ceiling perception was effective in solving these problems.

• Klíčová slova
• Glass ceiling,
• MeSH
• lidé MeSH
• pracoviště MeSH
• pracovní podmínky psychologie   MeSH
• profesní mobilita * MeSH
• průřezové studie MeSH
• sexismus MeSH
• zdravotní sestry * psychologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH
• Geografické názvy
• Turecko MeSH

BACKGROUND: Observational data on composite scores often comes with missing component information. When a complete-case (CC) analysis of composite scores is unbiased, preferable approaches of dealing with missing component information should also be unbiased and provide a more precise estimate. We assessed the performance of several methods compared to CC analysis in estimating the means of common composite scores used in axial spondyloarthritis research. METHODS: Individual mean imputation (IMI), the modified formula method (MF), overall mean imputation (OMI), and multiple imputation of missing component values (MI) were assessed either analytically or by means of simulations from available data collected across Europe. Their performance in estimating the means of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), and the Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) in cases where component information was set missing completely at random was compared to the CC approach based on bias, variance, and coverage. RESULTS: Like the MF method, IMI uses a modified formula for observations with missing components resulting in modified composite scores. In the case of an unbiased CC approach, these two methods yielded representative samples of the distribution arising from a mixture of the original and modified composite scores, which, however, could not be considered the same as the distribution of the original score. The IMI and MF method are, thus, intrinsically biased. OMI provided an unbiased mean but displayed a complex dependence structure among observations that, if not accounted for, resulted in severe coverage issues. MI improved precision compared to CC and gave unbiased means and proper coverage as long as the extent of missingness was not too large. CONCLUSIONS: MI of missing component values was the only method found successful in retaining CC's unbiasedness and in providing increased precision for estimating the means of BASDAI, BASFI, and ASDAS-CRP. However, since MI is susceptible to incorrect implementation and its performance may become questionable with increasing missingness, we consider the implementation of an error-free CC approach a valid and valuable option. TRIAL REGISTRATION: Not applicable as study uses data from patient registries.

• MeSH
• ankylózující spondylitida MeSH
• axiální spondyloartritida * MeSH
• C-reaktivní protein analýza   MeSH
• interpretace statistických dat MeSH
• lidé MeSH
• stupeň závažnosti nemoci MeSH
• výzkumný projekt MeSH
• zkreslení výsledků (epidemiologie) MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH