Structured Form Research Articles

Entrepreneurial leadership and organizational performance: employee creativity and behavior

PurposeThis research aims to highlight the connection of entrepreneurial leadership with improved organizational outcomes through employee-driven factors of creativity and behavior. It addresses certain existing research gaps concerning the interaction of leadership practices with organizational performance.Design/methodology/approachA quantitative cross-sectional design was employed to analyze the interaction among the study variables. Data was collected from 414 employees across various industries in Punjab, Pakistan through an adapted questionnaire which was in structured form. Smart Pls 4 and SPSS were used for analysis of the collected data.FindingsThe findings indicate positive and significant effect of entrepreneurial leadership on organizational performance, wherein employee creativity and behavior are the key mediators. The study shows that high levels of employee creativity and positive behavior are directly linked to improved performance metrics in organizations led by entrepreneurial leaders. This consequently underscores the need of creating an environment which encourages creativity and supports positive employee behaviors required for entrepreneurial leadership.Originality/valueThis research enriches the academic discourse by quantitatively confirming the mediating role of employee creativity and behavior between entrepreneurial leadership and organizational performance. Unlike previous studies which focused mainly on direct effects or less quantifiable leadership aspects, this study provides empirical evidence supporting a model where employee attributes significantly impact organizational success under entrepreneurial leadership. This insight is valuable for leaders and practitioners aiming to utilize entrepreneurial leadership in dynamic business settings.

LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations.

Lifestyle factors (LSFs) are increasingly recognized as instrumental in both the development and control of diseases. Despite their importance, there is a lack of methods to extract relations between LSFs and diseases from the literature, a step necessary to consolidate the currently available knowledge into a structured form. As simple co-occurrence-based relation extraction (RE) approaches are unable to distinguish between the different types of LSF-disease relations, context-aware models such as transformers are required to extract and classify these relations into specific relation types. However, no comprehensive LSF-disease RE system existed, nor a corpus suitable for developing one. We present LSD600 (available at https://zenodo.org/records/13952449), the first corpus specifically designed for LSF-disease RE, comprising 600 abstracts with 1900 relations of eight distinct types between 5027 diseases and 6930 LSF entities. We evaluated LSD600's quality by training a RoBERTa model on the corpus, achieving an F-score of 68.5% for the multilabel RE task on the held-out test set. We further validated LSD600 by using the trained model on the two Nutrition-Disease and FoodDisease datasets, where it achieved F-scores of 70.7% and 80.7%, respectively. Building on these performance results, LSD600 and the RE system trained on it can be valuable resources to fill the existing gap in this area and pave the way for downstream applications. Database URL: https://zenodo.org/records/13952449.

Repeatable process for extracting health data from HL7 CDA documents.

This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes. We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format. This pipeline was designed to adapt to continuously updated data exchange format changes and handle various CDA document subsets for different scientific studies. We demonstrated via selected use cases that our pipeline successfully transformed a significant portion of diagnosis codes, body weight and eGFR measurements, and PAP test results from CDA documents into OMOP CDM, showing the ease of extracting structured data. However, challenges such as harmonising diverse coding systems and extracting lab results from free-text sections were encountered. The iterative development of the pipeline facilitated swift error detection and correction, enhancing the process's efficiency. After a decade of focused work, our research has led to the development of an ETL pipeline that effectively transforms HL7 CDA documents into OMOP CDM in Estonia, addressing key data extraction and transformation challenges. The pipeline's repeatability and adaptability to various data subsets make it a valuable resource for researchers dealing with health data. While tested on Estonian data, the principles outlined are broadly applicable, potentially aiding in handling health data standards that vary by country. Despite newer health data standards emerging, the relevance of CDA for retrospective health studies ensures the continuing importance of this work.

Linkages of managing talent and succession planning in selected listed firms: A board of director’s vision

To explore and analyse the linkages between talent management strategies (TMS) and succession planning interventions in corporate organizations. A descriptive research design has been utilized to conduct the study based on cross-section panel data collected from senior-level human resources professionals associated with listed firms. Their insights have been collected through a structured questionnaire in Google Form. The study offers that as a part of TMS, the formal system for career aspiration does not necessarily indicate that there is a readymade/proactive succession plan exists in the listed firm. Further, it reveals from top management executives that if offered a development program, then it creates a greater chance for effective succession. Further, it occurs that standard talent reviews don’t necessarily influence the succession planning process in corporate firms. The present study will be beneficial for corporate companies and startups to plan their agenda for having the right talent and good successors on key management profiles, like appointing candidates to board-level positions. Even, a sound business continuity plan can be portrayed with having a proactive leadership succession in place. This study offers fresh perspectives on the board of director’s perspectives on managing talent and succession planning. The study is a first-of-its-kind attempt to investigate succession planning and TMS in listed corporate entities in an empirically designed methodology. The study is based on a small respondent size of 130. Findings might differ when a larger sample is analysed. Executive reluctance is another limitation while collecting data in a structured form.

The Statutory Format of Structured Financial Statements Has Limited Impact on the Profit Tax Determination and Shall Not Be Used as an Argument for Not Allowing the Voluntary Use of IFRS. Evidence from Slovakia

Objective: The objective of this research was to verify whether it is useful to proceed with financial statements in the legally ordered form to determine the profit tax. Theoretical Framework: Financial statements in Slovakia display the amount of profit tax without any information about how the tax has been determined. Method: Despite the fact that the tax rate is given by the law and the tax base is calculated from the financial profit before taxation, we have examined whether structured financial statements bear the information required to determine or verify the profit tax amount. On the random sample of publicly available financial statements from Slovakia we examined whether any of the 201 disclosed variables correlate to the profit tax and whether disclosed figures could be used to determine or verify profit tax. Results and Discussion: We have shown that requiring the submitting of official financial statements in a legally ordered structured form, has no added value regarding to profit tax. Research Implications: Profit tax determination from financial statements cannot be used as a relevant argument to require a legally ordered structured form of financial statements. Companies should be given the possibility of issuing their financial statements based on IFRS or IFRS for SMEs, if the company wishes to do so in order to better display their financial statements. Originality/Value: This research shows that legal requirements on financial statements shall be regularly evaluated whether their structure is still relevant considering technological progress, information value and effort companies, especially SMEs, must expend.

Prevalence of Duchenne muscular dystrophy in Italy: a nationwide survey.

The availability of care recommendations has improved survival and delayed the progression of clinical signs in Duchenne muscular dystrophy. The aim of the study was to perform a nationwide survey investigating the prevalence, age distribution, and functional status of Duchenne muscular dystrophyin Italy. The survey was performed by collecting data from all 31 reference centers for Duchenne muscular dystrophy in Italy using a structured form. We assessed age distribution, motor function, and the need for respiratory and nutritional support to evaluate their prevalence in different age and functional subgroups. The estimated prevalence was 1.65/100,000 (3.4/100,000 males). There were 972 boys and adults with a confirmed diagnosis of Duchenne, of age ranging between 6months and 48years (mean = 16.5). Over 59% were below the age of 18years and the remaining 41% were adults. Over 43% were ambulant and 57% non-ambulant; 14.7% were steroids naive (mean 20.6years), 75% are currently on steroids (mean 14.6years) with 604 on the daily regime, 126 intermittent. Nearly 73% did not require any ventilatory support, 16% had NIV ≤ 12h, 9% > 12h, and 1.4% had a tracheostomy. More than 82% did not require any nutritional support, 13% required food modification/semisolid and 4.4% had a G-tube. Our findings provide information to be used not only for epidemiological purposes but also for possible trial design to include older non-ambulant patients who until recently have been excluded and for whom clinical information is limited. What is Known • Duchenne muscular dystrophy is a progressive disorder associated with reduced survival. • As part of the disorder there is also a progressive loss of important milestones, including loss of ambulation, and increased need for respiratory and nutritional support. What is New • Our nationwide survey provides prevalence, age distribution, and functional status for Duchenne muscular dystrophyin Italy including both boys and adults. • Our findings can be used for epidemiological purposes and for possible trial design.

EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN PREDICTING CRYPTOCURRENCY PRICES UNDER MARKET VOLATILITY: A STUDY BASED ON THE USA FINANCIAL MARKET

The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.

Exploring ultrasound and electromyography for carpal tunnel syndrome diagnosis: a comprehensive comparative study and implications for occupational medicine.

To assess the contribution of ultrasound in diagnosing occupational carpal tunnel syndrome (CTS), compare it with electromyography (EMG) results, and evaluate the ultrasound characteristics of CTS patients. A nine-month cross-sectional study (January-September 2021) involved CTS patients and a control group, utilizing a structured form for data collection. EMG was performed on the patient group ('cases') and ultrasound examinations were conducted on both groups. Statistical analysis was performed using SPSS software. Among 44 cases and 30 controls, CTS patients (mean age 44.9 years) exhibited predominantly bilateral symptoms (90.9%). The optimal cross-sectional area (CSA) threshold for diagnosis was 10.3 mm2 (89% sensitivity, 84% specificity). Significant differences in ultrasound criteria were observed between patient and control groups, including the "notch sign" (p = 0.012), hypoechoic appearance (p = 0.016), and reduced median nerve mobility (p = 0.021). Quantitatively, CSA (13.7 mm2 vs. 7.4 mm2), flattening ratio (3.3 vs. 2.1), and retinaculum bulging (3.2 mm vs. 1.9 mm) significantly differed between cases and controls (p = 0.0019, 0.025, and 0.01, respectively). Positive Phalen tests correlated with higher CSA (p = 0.005) and retinacular bulging (p = 0.02). CSA correlated with EMG parameters, indicating slower conduction velocities, lower amplitudes, and longer latencies (p < 10^(-3), r = -0.56, -0.62, -0.36, and -0.68, respectively). This study highlights ultrasound's diagnostic potential for CTS, particularly in occupational settings. Its non-invasiveness and reliability advocate for its integration into routine diagnostic protocols, supporting evidence-based management strategies. Further research is needed to explore long-term efficacy and broader applicability.

Studies on Multifunctional Nanostructured Materials

Multifunctional materials are of today’s quest. Miniaturization, i.e. development of these materials in the form of nanomaterials is of primary need considering their application in devices. Moreover, if these are obtained in nanostructured form, they can bring wonders. We have adopted a method for developing multiferroic BiFeO3 (BFO) with simultaneous antiferromagnetic, ferroelectric &amp; ferroelastic behaviour in form of nanostructures like nanorods, nanowire etc. by employing Anodised Alumina (AAO) template with various pore sizes from 20nm with solution route followed by controlled vacuum filtration and sintering. Diameters of nanorods are in the range of 20-100 nm as observed by FESEM. Capacitance assayed by cyclic voltammetry (CV) and charge discharge processes reveals a very high value of specific capacitance of 450F/gm. Capacitance has been estimated by extrapolating the charge collected at the electrode to that at scanning rate of infinity which is relevant for the charge collected at the nanorods protruding out of the template. Charging and discharging times are quite constant over a large number of cycles. This large value of specific capacitance can be attributed to the nanostructure form of BFO nanorod. The high value of specific capacitance of BFO nanorods brings forth its use as electrode in storage energy devices. Also, a high value of polarization as well as a significant magnetic susceptibility are observed in multiferroic Bismuth Ferrite (BFO) in the form of nanorods protruding out. The high values of polarization and magnetic susceptibility are attributed to the structured form of BFO nanorods giving rise to the directionality. There is no leakage current in P-E loop examined at various fields and frequencies. Magnetocapacitance measurements reflect a significant enhancement in magnetoelectric coupling also.

Let’s Discover More API Relations: A Large Language Model-Based AI Chain for Unsupervised API Relation Inference

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus, and are affected by the characteristics of the input text. To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design an AI chain consisting of three AI modules: API Fully Qualified Name (FQN) Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN Parser and API Relation Decider is 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach’s inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method’s average F1 value of 0.40. Compared to the original CoT and modularized CoT methods, our AI chain design has improved the performance of API relation inference by 71% and 49%, respectively. Meanwhile, the prompt ensembling strategy enhances the performance of our approach by 32%. The API relations inferred by our method can be further organized into structured forms to provide support for other software engineering tasks.

Features of educational and enlightening online video content in Russia

INTRODUCTION. The active development of the audiovisual sector of new media and its integration into the system of educational and enlightening practices makes the issue of specifying the key features of modern online educational and enlightening video content relevant. The purpose of the study is to attempt to give a systemic description of this type of content.MATERIALS AND METHODS. The object of the study is educational and enlightening video content presented in the Russian-language sector of the Internet. The chronological framework of the study is 2022–2023. The key research methods are comparative-typological, historical-functional, and textual.RESULTS AND DISCUSSION. Despite significant overlaps in the area of implemented functions, educational and enlightening content are not identical. The former is usually offered by educational organizations in a structured form with a clearly defined goal of forming specific knowledge, skills, and abilities. The latter is content aimed at satisfying the user’s interest or curiosity in a particular area, offering information of interest to the user for the purpose of self-development and not aimed at forming specific knowledge, skills, and abilities. These types of content have significant differences in such typological features as goal setting, audience focus, genre and thematic content.CONCLUSION. The study revealed significant differences between educational and enlightening online video content, which generally consist of the fact that educational content largely copies educational genres and formats, and also gravitates toward classical video formats, while enlightening content is more focused on approaches developed in video blogging.

Docosahexaenoic Acid (DHA) Supplementation in a Triglyceride Form Prevents from Polyglutamine-Induced Dysfunctions in Caenorhabditis elegans.

A common hallmark of neurodegenerative diseases is the accumulation of polypeptide aggregates in neurons. Despite the primary cause of these diseases being inherently genetic, their development can be delayed with proper preventive treatments. Long-chain polyunsaturated fatty acids (ω-3 LCPUFA) are promising bioactive nutrients that are beneficial for brain health. In this study, the impact of an oil rich in a structured form of docosahexaenoic acid (DHA) triglyceride (TG) was assessed in a Caenorhabditis elegans model expressing long poly-glutamine (polyQ) chains, which mimics the symptomatology of polyQ-related neurodegenerative diseases such as Huntington's disease (HD), among others. The lifespan, the motility, the number of polyQ aggregates, the oxidative stress resistance, and the cognitive performance associated with sensitive stimuli was measured in mutant nematodes with polyQ aggregates. Overall, DHA-TG at 0.5 µM improved the lifespan, the motility, the oxidative stress resistance, and the cognitive performance of the nematodes, emphasizing the protection against serotonergic synapse dysfunction. Furthermore, the treatment reduced the polyQ aggregates in the nematodes. The data described herein shed light on the connection between DHA and the cognitive performance in neurodegenerative diseases and demonstrated the potential of DHA-TG as nutritional co-adjuvant to prevent the development of polyQ-associated dysfunctions.

Using music to reduce anxiety and stress in patients undergoing coronary angioplasty: A randomized clinical trial protocol

In the current health context, there is a widespread increase in chronic non-communicable diseases that predominantly affect the cardiovascular system. Recent epidemiological data in Brazil indicate a rise in these diseases, which can result in severe harm to patients, including death. This study aims to present a study protocol to evaluate the effect of music in reducing anxiety and stress in patients undergoing coronary angioplasty. A randomized, controlled, double-blind clinical trial will be conducted with 52 patients undergoing coronary angioplasty. The patients will be randomly assigned to one of two groups: a Control Group or an Experimental Group, to assess the effect of music on anxiety and stress. Two key points will be considered to evaluate the intervention's impact: data on patients' anxiety and stress, collected through structured forms, will be gathered both before and after the intervention, along with an assessment of vital signs. The primary expected outcome is a reduction in anxiety and stress, while the secondary expected outcome is stability in vital signs.

The Cross-Sectional Association of Health Literacy With Healthy Lifestyle Behaviors in the Turkish Population: A Systematic Review and Meta-Analysis.

To meta-analytically investigate the cross-sectional association between health literacy (HL) and healthy lifestyle behaviors (HLBs) in the Turkish population. Web of Science, PubMed, Scopus, EBSCO, DergiPark, TRDizin, and Türkiye Council of Higher Education Thesis Center. Not accessible in Turkish or English in full text; not on the Turkish population; addressed e-HL, digital HL, mobile HL or corporate HL; addressed HL in a specific context; did not seek any association between HL and HLBs. Cross-sectional studies that reported a correlation coefficient or any convertible equal statistical measure for the association between HL and HLBs. Two reviewers independently screened the articles, extracted the data from the included studies into a structured form and assessed their methodological quality. We conducted meta-analyses and reported the characteristics, outcomes, and quality assessment of studies. Of 3833 records accessed we included 52 that fulfilled the eligibility criteria. Our primary findings demonstrated positive low-to-moderate associations between HL and health-promoting behaviors (r = .32 [CI:.30-.35; I2 = .00]), HL and physical activity (r = .23 [CI:.18-.27; I2 = .12]), and HL and vaccination (r = .12 [CI:.07-.17; I2 = .00]). In contrast, HL was not significantly associated with non-smoking (r = .02 [CI:-.02-.36; I2 = .09]), healthy diet (r = .02 [CI:-.01-.06; I2 = .00]), and screening (r = .07 [CI:.04-.11; I2 = .07]). However, our sensitivity and meta-regression analysis results suggested that the association of HL with healthy diet seems to increase with age. On the other hand, we failed to find any convincing evidence of any of the included variables affecting the association between HL and all types of HLBs (r = .17, I2 = 95.81%). HL has the potential to improve the HLBs of Turkish individuals, however, there is a need for better understanding of the possible underlying determinants of the association between them.

Antepartum Cardiotocography and Feto-maternal Outcome Among Admitted Patients in Bangabandhu Sheikh Mujib Medical University (BSMMU) Dhaka, Bangladesh

Background: Admission Cardiotocography (CTG) a noninvasive procedure, is used to indicate the state of oxygenation of the fetus on admission into the labor ward. This study is performed to assess the association of admission CTG findings with feto-maternal outcome at a tertiary health facility in Bangladesh. Materials and methods: A prospective, observational study of 50 pregnant women who were admitted into the labor ward with singleton live pregnancies in Bangabandhu Sheikh Mujib Medical University. Information on the demographic characteristics, obstetrics and medical history, admission CTG tracing and neonatal outcome was obtained using a structured data collection form. Data were analyzed using the SPSS software version 20.0 with the level of significance set at p&lt; 0.05. Results: The admission CTG findings were normal in 72% and abnormal in 28% of the women. The occurrence of low birth weight, asphyxiated neonates and Special Care Neonatal Unit (SCANU) admission was significantly more frequent among those with abnormal admission CTG results compared with normal results (p&lt;0.001 for each). The incidence of cesarean delivery was more common when the CTG findings were abnormal, whereas all women with normal CTG result had a vaginal delivery (p&lt;0.001). Conclusion: Admission CTG was effective in identifying fetuses with a higher incidence of perinatal asphyxia. Neonatal outcome such as low birth weight, APGAR score and SCANU admission was significantly associated with pathological CTG findings. In the absence of facilities for further investigations, prompt intervention for delivery should be ensured if admission CTG is pathological. IAHS Medical Journal Vol 7(1), June 2024; 50-53

Abstract 4125093: Association of Systemic Inflammatory Index and Systemic Inflammatory Response Index with Major Adverse Cardiovascular Events in Patients with Acute Coronary Syndrome Who Had Primary Percutaneous Coronary Intervention

Introduction: Inflammation plays an important role in the pathogenesis of coronary artery disease and Acute Coronary Syndrome (ACS). Inflammatory indicators such as neutrophil count and monocyte count potentially may predict patients’ outcomes and prognosis in ACS. White blood cells count is an affordable and accessible way to assess the systemic immune response. Considering their multidirectional effect on atherosclerosis, new inflammatory biomarkers integrating various leukocyte subgroups have been proposed to calculate the systemic inflammatory response index (SIRI) and systemic inflammatory index (SII). Aim: The aim of this study was to assess the association between the systemic inflammatory response index (SIRI) and Systemic inflammatory index (SII) at admission with major adverse cardiovascular events (MACE) 30-days after Primary Percutaneous Coronary Intervention (PPCI) among patients with Acute Coronary Syndrome (ACS) at one referral hospital in Jordan. Method: A Prospective design was used to assess and follow 150 adults presented with ACS and received PPCI at one referral hospital in Jordan. Data was collected from patients and their medical records during hospital stay using a structured data collection form. The MACE was assessed 30-days after PPCI through phone calls with patients and confirmed from medical records. Results: The study included 150 patients with ACS, 82.7% (n= 124) of them were males. The mean age of patients was 57.68 (SD= 11.19) years. Types of ACS include stable angina 5.3% (n=8), unstable angina 24% (n=36), NSTEMI 28.7% (n=43), and STEMI 24% (n=36). All patients had interventional PCI with balloon and stent insertion. The mean of SIRI was 24.45 (SD=25.49) and the mean of SII was 663.30 (SD= 403.75). Rate of MACE as composite outcome 30-days after PPCI as 24.7% (n=37). The most MACE was unplanned repeat revascularization (n=14, 38% of MACE). Significant association between SIRI and SII, and MACE was found SIRI:(Χ 2 (2)&gt; = 12.63, p = 0.001), SII:(Χ 2 (2)&gt; = 4.23, p = 0.03) . Conclusion: The SIRI and SII are inflammatory biomarkers that should be studied as potentially predictors of MACE among patients presented with ACS.

Analysis of the Arab singer Shadia’s lyrics

AbstractIn this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely "Image missing" (Shadia). Lyrics analysis provides a deep insight into the artist’s career evolution and her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis, we had to collect and compile the lyrics of Shadia accompanied with the necessary meta-data into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens conveys much more information than the source words. We performed a lexical analysis in order to study both the lexical diversity and density over the course of Shadia’s career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds and more quantitative measures such as term frequency–inverse document frequency. We have divided the career life of Shadia into sub-decades of length 5 years, and all analyses are done both in a yearly fashion and more coarsely over such sub-decades. Our quantitative analyses show strong correlations between the artistic lyrical work of Shadia and the state of affairs in Egypt and the Arab World during her time. In particular, Shadia’s lyrics reflect the radical changes in all aspects of the social, political, and economic conditions. This is especially relevant knowing that Shadia is very much truly considered the daughter of the generation of the 1952 revolution in Egypt. The significance of Shadia and her lyrics stem essentially from being contemporaneous to radical changes in Egypt across all sectors including political (support of liberation movements across the world and the conflict with Israel) and socioeconomic (especially changing the social class structure in Egypt). We also investigated the potential effectiveness of PoS (Part-of-Speech) tagging in genre analysis and classification.

Evaluating the Development of Pre-Service Primary School Teachers’ Competences in the Context of a Student-Centred Science Visits Course

The student-centred approach emphasises active learning, where students take responsibility for their own learning process, in which they are actively involved and integrate new information into their existing knowledge framework. Combining this approach with outdoor learning provides an even more effective teaching strategy. When combining these approaches, the individual needs and interests of the students must be taken into account. The research was conducted as part of The Science Visits Course among 30 prospective primary school teachers in their final years of study. The focus of the study was on the students’ self-assessment of their competences in science education and on the implementation of a student-centred teaching process, including outdoor teaching. Data were collected using online questionnaires and a structured evaluation form to assess students’ plans for a science visit. The research findings suggest that students find this type of work engaging and that it encourages their self-engagement, taking responsibility and working together in a group through sharing ideas and opinions. On the other hand, the student competences important for outdoor education have come to the fore to some extent, but there are still areas that can be improved, especially in the area of science content knowledge.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Adherence to infliximab treatment in patients with immune-mediated inflammatory diseases from a referral center in Brazil: a cohort study

BackgroundInfliximab therapy is effective in controlling symptoms and attaining clinical remission of immune-mediated inflammatory diseases. However, treatment adherence is essential to achieve the therapeutic objective. This study aimed to determine the rate of adherence to infliximab treatment in patients treated at a referral center at a university hospital.MethodThis ambispective cohort study included patients treated at the Professor Edgard Santos University Hospital (HUPES) referral center of our university hospital between March 2022 and February 2023. Sociodemographic, clinical, and pharmacotherapeutic data were collected from 101 patients through interviews and medical record reviews using a structured form. The adherence rate was defined as the proportion of days covered in a year. Patients who achieved an adherence rate > 80% were considered adherent.ResultsThe treatment adherence rate was 91.04%. Individuals with inflammatory bowel diseases had a 39.1% higher risk of non-adherence to treatment compared with other patients in our sample (p < 0,05). Most patients achieved remission or control of the underlying disease activity and had good functional capacities. The main reason for absence on the scheduled date was difficulty traveling to the referral center.ConclusionsDespite the reported difficulties, treatment adherence was observed to be high. As the study was conducted in a reference unit with multidisciplinary care and continuous monitoring for treatment effectiveness, safety, and adherence, welcoming and good communication between professionals and patients may have contributed to the high adherence rate.