Data Analysis Software Research Articles

Perception and coping mechanisms of patients with diabetes mellitus during the COVID-19 pandemic in Ibadan, Nigeria.

The 2019 coronavirus disease (COVID-19) ushered in a period of fear and uncertainty, resulting in structural instability across the globe. Vulnerable individuals, such as patients with diabetes mellitus, are predispose to have adverse effects and complications of COVID-19 when infected. We explored the perception of diabetes mellitus patients during the COVID-19 pandemic and their coping mechanisms at the University College Hospital, Ibadan. We employed an exploratory qualitative study design to explore diabetes mellitus patients' perceptions and coping mechanisms during the COVID-19 pandemic. A purposive sampling technique was used to recruit 32 participants (2 health professionals and 30 diabetes mellitus patients). In-depth interviews were used to collect the data from the participants. All the recorded audio data were transcribed verbatim and exported to NVivo software for thematic data analyses. Most diabetes mellitus patients were not fearful of the pandemic but were optimistic that it would not affect their health. Mechanisms such as the usage of herbal medicines and adherence to COVID-19 precautionary measures were noticed among patients. The study also revealed that the hospital's coping mechanism during the COVID-19 pandemic include prolonged appointments, limiting the number of patients attended per clinic day, and the provision of telehealth service. Patients in our study utilised negative coping mechanisms such as reduced drug dosages, subscriptions to cheaper drug brands, and reliance on religious institutions rather than a clinic for health instructions. The study has shown that diabetes mellitus patients were not fearful of the COVID-19 pandemic. The utilisation of telehealth, encouragement of daily monitoring of sugar levels, provision of avenues for a medication review, and adherence to the safety protocols were coping mechanisms employed by the health system and diabetes mellitus patients. We recommend that the government and other healthcare stakeholders reinforce the resilience of diabetes mellitus patients by alleviating their health burdens during the pandemic. This could be done by subsidizing the prices of drugs, tests, and consultation fees for patients with diabetes mellitus. Also, more efforts should be made to elevate the health system through the reduction in waiting and appointment times in the diabetes clinic and employing more health personnel in the clinic.

Kindergarten teachers’ perspectives on sustainable development practices and observed practices

Purpose: Kindergartens are an important educational environment for the development of children at an early age, and they also play a crucial role in developing the values of sustainable development. The purpose of this study is to investigate kindergarten teachers’ perceptions of observable and sustainable development practices. Design, methodology, approach: Semi-structured interviews were conducted with 302 Saudi kindergarten teachers. Additionally, observation cards were utilized to collect data on actual practices of sustainable development in kindergartens. Data were analyzed using Nvivo12, a qualitative data analysis software, and descriptive analysis methods. The main themes were produced first, and then the perspectives were organized around them. Finding: The impact of social and cultural factors on the development of values, the lack of resources available to implement educational activities, and teacher awareness and training gaps were found to be the main barriers to the development of sustainable development values in kindergartens. Originality, value: To the best of the author’s knowledge, this is the first study in Saudi Arabia that has looked into the environmental and social perceptions of early childhood teachers about sustainable development practices, so the study’s findings can highlight the importance of reorienting teacher education programs toward sustainability in order to bridge knowledge and practice gaps.

Transformative value, communities and service designs for sustainability

PurposeThe purpose of this transformative service research (TSR) is to apply, innovate on and extend the understanding of service-dominant logic (SDL) perspectives, sustainable service ecosystem design ideas, transformative value and meeting sustainable development goals (SDGs). This study explores these through volunteers’ lived experiences and their perceived health and well-being outcomes in the context of botanic gardens as health-care service settings.Design/methodology/approachA total of 3 UK botanic gardens and 84 volunteers between 22 and 87 years of age participated in this qualitative study. Volunteering stories were collected through emails, telephone exchanges, online and in-person interviews, free-flowing discussion and field observations. These were coded and analysed by using computer-assisted qualitative data analysis software, NVivo 14 Plus and Leximancer. Thematic analysis facilitated the mapping of well-being outcomes highlighting transformative value against existing health and well-being indices.FindingsInsights extend knowledge into SDL, TSR and transformative value experienced by volunteers across three UK botanic garden service ecosystems. Environmental, organisational and personal factors, and physical, mental and social health outcomes are presented to emphasise transformative value experienced, especially in retiree volunteers. Theoretical contribution is in the form of empirical evidence to support and extend insights about transformative value and more so, significant epistemological change and meeting SDGs in botanic gardens. Results add to contemporary TSR on health-care-related well-being outcomes and ideas regarding sustainable service ecosystem design.Research limitations/implicationsIt is recommended that service research be extended across other botanic gardens, as well as other novel underexplored contexts for comparative studies of transformative value. Continued development and consideration of service designs as ongoing efforts to redefine and reimagine services marketing innovation for botanic gardens are recommended. Botanic gardens are complex service ecosystems worthy of rigorous service research to capture and measure the impact and outcome of ongoing work of the sector in advancing SDGs and having a transformative effect on individual and societal health and well-being.Practical implicationsThis study highlights opportunities for greater area-based, coordinated, collaborative, multi-stakeholder services marketing partnerships for strategic sustainable service ecosystem design for the botanic gardens and health-care sectors. These sectors can make better use of service research and marketing to further innovate and co-develop health and well-being strategies, campaigns and opportunities to develop services to transform and influence positive health and well-being outcomes for people. Results reveal greater opportunities for collaborative partnership and services marketing’s role and practice for the ongoing vitality and viability of botanic gardens. Joint efforts would enable innovation on sustainable service ecosystem design, advancing SDGs and improving life on planet Earth.Social implicationsTransformative value linked to newfound life experiences and meaning to life after retiring with a range of factors, and health and well-being outcomes were prominent. Social connections to the wider community were present, revealing links to a range of people who may not have traditionally had contact with botanic garden heritage and their strategic efforts. Therefore, it is services marketing opportunities for botanic gardens that hold one key to greater transformative value, sustainability and greater influence and impact on individual and societal health and well-being.Originality/valueTo the best of the author’s knowledge, this is the first TSR on botanic gardens as health-care service settings, resulting in a conceptual framework on transformative value and well-being outcomes in meeting SDGs. It extends insights on SDL, sustainable service ecosystem design and roles of marketing for the common good. Botanic gardens are unique research institutes, highly acclaimed for research, conservation, education and displays of special botanical collections, as well as providing health care, among other impactful SDG opportunities. This can be made more explicit through ecosystemic thinking, service research and integrated services marketing of botanic garden’ roles and contributions worldwide.

A Meta-Analysis of Ultrasound Guided Nerve Blocks for Enhanced Recovery in Adult Cardiac Surgery Patients

Background: Ultrasound-guided nerve blocks can both reduce intraoperative opioid use and pain scores. However, its role in enhancing postoperative recovery for adult cardiac patients requires further investigation. This study examines the impact of ultrasound-guided nerve block on adult cardiac patients' recovery. Methods: We conducted a systematic search for randomized controlled trials (RCTs) published between 2018 to 2022, focusing ultrasound-guided nerve block in adult cardiac surgery. The search included Pubmed, Embase, and Cochrane databases, targeting studies on elective thoracotomy. The outcomes analyzed included postoperative extubation time, intensive care unit (ICU) stay time, and length of hospital stay (LOS), using Review Manager software (Review Manager 5.4, The Cochrane Collaboration, 2020, Beijing, China) for data synthesis and analysis. Results: Out of 26 RCTs, eight studies involving including 424 subjects were included in this meta-analysis. The results showed that ultrasound-guided nerve block significantly reduced postoperative extubation time (odds ratio [OR] = –2.16, 95% confidence interval [CI]: –3.05 to –1.26), ICU stay (OR = –1.17, 95% CI: –1.40 to –0.94), and overall hospitalization duration (OR = –0.96, 95% CI: –1.64 to –0.29). Conclusion: Ultrasound-guided nerve block significantly reduces the postoperative extubation time, ICU stay, and LOS, in adult cardiovascular surgery. These benefits contribute substantially to enhanced recovery after cardiac procedures. Registration: PROSPERO (CRD42023470545).

Method for Using Voicemail and Email for Qualitative Data Collection Among Nurses.

Memorializing nurses' experiences during the COVID-19 pandemic had the potential to allow scientists and policymakers to learn about the impact on the nursing profession and health care systems. Yet, nurses are considered a difficult population to recruit for research. To describe an innovative qualitative data collection method for capturing current practice experiences among nurses working during the COVID-19 pandemic. Guerilla theory served as the theoretical framework. Utilizing a qualitative descriptive design, a telephone voicemail messaging system was developed to capture nurses' experiences. Nurses were recruited with convenience and snowball sampling via social media and state listservs. The telephone voicemail messaging system, Twilio, was used. After listening to the recording of the consent form, the participants shared their experiences by leaving a voice message where they answered the prompt, "Tell us about your experiences working during the COVID-19 pandemic." Seventy voicemails were included, and the voicemails were transcribed. After a nurse shared their experience via an email sent to the research team, emails were added to the data collection; 16 emails were received. Transcripts and emails were uploaded to the qualitative data analysis software program, Dedoose, and coded by 2 researchers using content analysis. Main themes were derived and discussed among the research team. Allowing participants multiple modes of expressing their experiences promote inclusivity in data collection. Further development and standardization of this method is needed for future research.

Expression and Evolution of Public Opinion in Social Network VKontakte During COVID-19 Pandemic

This article analyzes comments on the social network VKontakte during the COVID-19 pandemic with the aim of uncovering public opinion and sentiments. A discursive model of comments on official materials related to the pandemic was constructed using the qualitative data analysis software tool Nvivo12. The Natural Language Toolkit (NLTK) was employed for this purpose. As a result of a comprehensive analysis of the evolution of "hot spots" of discussion, it was found that during the pandemic, comments in the VKontakte group "StopCoronavirus.rf" addressed several thematic directions: "political measures for pandemic prevention," "protection of the civilian population from the pandemic," "medical prevention of virus spread," "the pandemic and people's livelihoods," "credibility of epidemiological data," "pandemic trends," and "spiritual support and encouragement." Among these, users expressed the greatest concern regarding subtopics related to people's behavior in preventing the virus's spread, the veracity of government-released data, the scale of testing, as well as the reliability of vaccines, online education, and stress caused by the pandemic. As the pandemic progressed, the frequency of discussions on specific topics fluctuated. Negative sentiments within society exhibited a tendency to rise, then decrease, and subsequently rise again, mirroring the overall trends of increasing and decreasing morbidity rates across Russia.

Realist analysis of qualitative data in health professions education research.

Qualitative realist analysis is gaining in popularity in health professions education research (HPER) as part of theory-driven program evaluation. Although realist approaches such as syntheses and evaluations typically advocate mixed methods, qualitative data dominate currently. Various forms of qualitative analysis have been articulated in HPER, yet realist analysis has not. Although realist analysis is interpretive, it moves beyond description to explain generative causation employing retroductive theorising. Ultimately, it attempts to build and/or 'test' (confirm, refute or refine) theory about how, why, for whom, when and to what extent programs work using the context-mechanism-outcome configuration (CMOC) heuristic. This paper aims to help readers better critique, conduct and report qualitative realist analysis. We describe four fundamentals of qualitative realist analysis: (1) simultaneous data collection/analysis; (2) retroductive theorising; (3) configurational analysis (involving iterative phases of identifying CMOCs, synthesising CMOCs into demi-regularities and translating demi-regularities into program theory); and (4) realist analysis quality (relevance, rigour, richness). Next, we provide a critical analysis of realist analyses employed in 15 HPER outputs-three evaluations and 12 syntheses. Finally, drawing on our understandings of realist literature and our experiences of conducting qualitative realist analysis (both evaluations and syntheses), we articulate three common analysis challenges (coding, consolidation and mapping) and strategies to mitigate these challenges (teamwork, reflexivity and consultation, use of data analysis software and graphical representations of program theory). Based on our critical analysis of the literature and realist analysis experiences, we encourage researchers, peer reviewers and readers to better understand qualitative realist analysis fundamentals. Realist analysts should draw on relevant realist reporting standards and literature on realist analysis to improve the quality and reporting of realist analysis. Through better understanding the common challenges and mitigation strategies for realist analysis, we can collectively improve the quality of realist analysis in HPER.

Algorithm Validation for Quantifying ActiGraph™ Physical Activity Metrics in Individuals with Chronic Low Back Pain and Healthy Controls.

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of this study was to replicate ActiGraph™ algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. MATLAB code was developed to replicate ActiGraph™'s activity counts and step counts algorithms, to sum the activity counts into counts per minute (CPM), and categorize each minute into activity intensity cut points. A free-living validation was performed where 24 individuals, 12 cLBP and 12 healthy, wore an ActiGraph™ GT9X on their non-dominant hip for up to seven days. The raw acceleration data were processed in both ActiLife™ (v6), ActiGraph™'s data analysis software platform, and through MATLAB (2022a). Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, were all less than 2%. ActiGraph™ algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife™ and MATLAB, allowing researchers to analyze data from any accelerometer in a manner comparable to ActiLife™.

Using the providers’ perspective on video review of neonatal procedures to create a roadmap: a qualitative study

ObjectiveTo examine the providers’ perceptions and experiences on implementation of video review (VR) of procedures in the neonatal intensive care unit (NICU).DesignQualitative study using semi-structured interviews with neonatal care providers...

SPSS vs. AMOS: A Comparative Approach to Analyzing Moderators in Behavioral Research

Many researchers face confusion in selecting appropriate software for quantitative data analysis due to the lack of comparative studies in this area (1). This study examines the moderator analysis results conducted using AMOS and SPSS software. Moderator analysis assesses the extent to which the independent variable influences the dependent variable through a moderator variable. The research utilized data from a questionnaire completed by 383 adolescents in Ahvaz, Iran. The independent variables included the impact of COVID-19, parental attachment, and peer attachment, while emotional intelligence served as the dependent variable, with online networking acting as the moderator. Analysis using SPSS indicated that online networking moderated the relationship between all independent and dependent variables. Conversely, results obtained from AMOS revealed no significant moderating effect between the independent and dependent variables. Descriptive data indicated that students experienced low internet connectivity during the COVID-19 pandemic, suggesting that online networking may have had a limited capacity to moderate these relationships. Consequently, the findings derived from AMOS are regarded as more reliable than those from SPSS. The novelty of this research lies in its comparative analysis of AMOS and SPSS, alongside its pioneering examination of moderation within developmental psychology studies using AMOS. The results underscore AMOS's superior accuracy and reliability, as demonstrated by its alignment with the descriptive data and ability to provide clearer visualizations of variable relationships. This study strongly advocates for the application of AMOS in psychological research, particularly for complex interaction effects.

OOPS: Object-Oriented Polarization Software for analysis of fluorescence polarization microscopy images.

Most essential cellular functions are performed by proteins assembled into larger complexes. Fluorescence Polarization Microscopy (FPM) is a powerful technique that goes beyond traditional imaging methods by allowing researchers to measure not only the localization of proteins within cells, but also their orientation or alignment within complexes or cellular structures. FPM can be easily integrated into standard widefield microscopes with the addition of a polarization modulator. However, the extensive image processing and analysis required to interpret the data have limited its widespread adoption. To overcome these challenges and enhance accessibility, we introduce OOPS (Object-Oriented Polarization Software), a MATLAB package for object-based analysis of FPM data. By combining flexible image segmentation and novel object-based analyses with a high-throughput FPM processing pipeline, OOPS empowers researchers to simultaneously study molecular order and orientation in individual biological structures; conduct population assessments based on morphological features, intensity statistics, and FPM measurements; and create publication-quality visualizations, all within a user-friendly graphical interface. Here, we demonstrate the power and versatility of our approach by applying OOPS to punctate and filamentous structures.

Ceas: an R package for Seahorse data analysis and visualization.

Measuring cellular energetics is essential to understanding a matrix's (e.g. cell, tissue, or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization. ceas is available on CRAN (https://cran.r-project.org/package=ceas). Source code and installable tarballs are freely available for download at https://github.com/jamespeapen/ceas/releases/ under the MIT license. Package documentation may be found at https://jamespeapen.github.io/ceas/. ceas is implemented in R and is supported on macOS, Windows and Linux.

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.

Portable genomic sequencers such as Oxford Nanopore's MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4- to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.

High Throughput Quality Control Tools for Carbon-Related Materials: A Raman Approach

Carbon-based materials play a pivotal role in battery technology, serving as negative electrode materials, conductive additives, and coating layers for both positive and negative electrode materials. Traditional characterization methods, such as transmission electron microscopy (TEM) and X-ray diffraction (XRD), fall short in providing comprehensive quality control, especially when distinguishing the structure of amorphous carbon materials. Raman spectroscopy as a convenient, simple tool is widely used to study the structure of carbon materials. It provides insights into the degree of crystallization, doping degree, and subtle structural differences arising from various preparation processes. By quantifying Raman spectrum testing results through statistical methods and providing relevant statistical outcomes, we demonstrate the potential of Raman spectroscopy as a quality control method for carbon material characterization. With extensive sample testing and automation, along with standardized spectral data processing and analysis software, we propose Raman spectroscopy as a suitable tool for high-throughput quality control in industrial production.

Building spectral templates and reconstructing parameters for core-collapse supernovae with CASTOR

ABSTRACT The future of time-domain optical astronomy relies on the development of techniques and software capable of handling a rising amount of data and gradually complementing, or replacing if necessary, real observations. Next-generation surveys, like the Large Synoptic Survey Telescope, will open the door to the new era of optical astrophysics, creating, at the same time, a deficiency in spectroscopic data necessary to confirm the nature of each event and to fully recover the parametric space. In this framework, we developed Core collApse Supernovae parameTers estimatOR (CASTOR), a novel software for data analysis. CASTOR combines Gaussian process and other machine-learning techniques to build time-series templates of synthetic spectra and to estimate parameters of core-collapse supernovae (CCSNe) for which only multiband photometry is available. Techniques to build templates are fully data driven and non-parametric through empirical and robust models, and rely on the direct comparison with a training set of 111 CCSNe from the literature. Furthermore, CASTOR employees the real photometric data and the reconstructed synthetic spectra of an event to estimate parameters that belong to the supernova ejecta, to the stellar progenitor and to the event itself, in a rapid and user-friendly framework. In this work, we provide a demonstration of how CASTOR works, studying available data from SN 2015ap and comparing our results with those available in literature.

Assessing the Dynamics of Sukuk Research on Scopus Database: A Bibliometric Analysis and Systematic Literature Review

<p><em>This research aims to determine the dynamics of sukuk research on the Scopus database. This research has three novelties: choosing more complex keywords, standardizing keywords, and explaining complex future research findings. This research employs a mixed method, using both quantitative and qualitative approaches. The quantitative method in this research uses simple statistical techniques through bibliometric analysis. Meanwhile, the qualitative method uses a systematic literature review approach. This research uses secondary data taken from the Scopus database. The data analysis software used is VOSviewer. The research results show that based on its distribution, research on sukuk was first published in 2007, and as of October 2023, there were 524 documents. Based on productivity, the most productive writer is Nader Naifar from Imam Mohammad Ibn Saud Islamic University (14 documents), Malaysia's most productive country (170 documents). Based on his popularity, the most popular author is Godlewski C.J. (186 citations). Based on trends, the frequently appearing keywords are sukuk, the newly used keywords are green finance, and the rarely used keywords are blockchain. Based on future research directions, there are recommendations for 96 topics that can be researched</em><em>. This research is beneficial for future sukuk research and supporting global sukuk practice.</em></p>

Sociological Study of Deurbanization Through Sustainable Village Development in Indonesia

This study aimed to analyze deurbanization through sustainable village development from a sociological perspective. The study was conducted in Bantaeng Regency, South Sulawesi, Indonesia. This area was selected because sustainable village development can change the number of "underdeveloped" and "very underdeveloped" villages into "developed" and "independent". A qualitative method was adopted to explore sustainable village development by referring to the village development index (IDM), namely social, economic, and environmental resilience. Data were collected through in-depth interviews with informants selected using purposive sampling. The data include elements of village government, community institutions, and society, such as community leaders, educators, religion, youth, traders, farmers, as well as representatives of women and poor groups. Observations were carried out at the study location while collecting documents related to regulations and reports on sustainable village development. The Qualitative Data Analysis (QDA) Software Program was used to analyze the data collected as well as write study reports. The results show that sustainable village development has led to the development of social, economic, and environmental resilience, which has an impact on deurbanization. Ruralization also occurred, where people living in the city moved to villages for economic, social, and cultural reasons, especially those who have kinship relations. In conclusion, private sector contributions can improve rural areas, because professional workers from cities move to villages to work in private companies, thereby supporting rural economic growth.

Role of Urinary Matrix Metalloproteinase-7 (MMP-7) as an Early Marker of Renal Dysfunction in Diabetic Individuals: A Cross-Sectional Study.

The matrix metalloproteinase 7(MMP-7) level gets heightened in the urine samples of diabetic individuals with impaired renal function. Renal biopsy is seldom offered because of its invasive nature. These concerns spurred the investigation of relationships between urine MMP-7 levels and the renal function of diabetic individuals. Studies exploring this aspect are scarce. We aimed to evaluate the glycemic and renal parameters of female and male individuals with or without type 2 diabetes mellitus (T2DM) or kidney disease. We also assessed the correlation of urine MMP-7 with various parameters. This prospective, analytical, cross-sectional study was conducted at Kalinga Institute of Medical Sciences (KIMS), Bhubaneswar, India, from February 2020 to January 2023. Female and male individuals 18-85 years of age diagnosed with either T2DM, hypertension, or kidney disease were assessed for their glycemic indices and renal parameters. Those with both renal disease and T2DMwere placed in group A. The diabetic individuals without kidney disease constituted group B. People in group C had neither kidney disease nor T2DM. Patients in group D had kidney disease but were not diabetics. The parameters of the male and female participants in each of the four groups were assessed and compared, including: age, body mass index (BMI), fasting blood sugar (FBS), glycosylated hemoglobin (HbA1c), serum urea, serum creatinine, estimated glomerular filtration rate (eGFR), urine albumin, urine creatinine, urine albumin-creatinine ratio (ACR), serum sodium, serum potassium, and urine MMP-7 levels. Furthermore, we correlated urine MMP-7 with all these traits. We used R software (version 4.4.0, Vienna, Austria) for data analysis. Two hundred eighty-seven (87.5%) of the 328 individuals we screened were eligible. Of them, group A had the maximum number (94) of participants, followed by B (75), C (65), and D (53). Males comprised 60.3% (n = 173) of the study population. The median age of the participants was 52.0 (44.0-61.1) years. The intergroup variations were statistically significant (p < 0.001) owing to their glycemic status and renal function. The gender-basis comparison of FBS and HbA1c yielded non-significant differences. On the contrary, assessment of the renal parameters revealed significant differences (p < 0.001) between females and males. The study population had a median urine MMP-7 level of 19.9 (1.1-50.5) µg/L. Significant associations with urine MMP-7 were found with serum creatinine (r = 0.91, p < 0.001), urine ACR (r = 0.86, p < 0.001), and eGFR (r = -0.84, p < 0.001). Our study portrayed that male diabetics, in comparison to female diabetics, had greater levels of urine ACR, urine MMP-7, eGFR, and serum creatinine. Moreover, urine ACR, eGFR, and serum creatinine strongly correlated with the urine MMP-7 level.

COVID-19 and lifelong learning: older adult peer educators’ perspective on the pandemic’s effect on adult education

ABSTRACT Background The COVID-19 pandemic ushered in a period of unprecedented uncertainty, profoundly affecting societies worldwide. Older adults often faced heightened risks of isolation and health complications. As research in the aftermath of the pandemic has sought to understand its multifaceted impacts, there remains a notable gap in examining older adults’ access to education, particularly within the Lifelong Learning Institutes (LLIs) or Universities of the Third Age (U3A). Methodology In the course of a broader study focusing on older adult peer educators, the topic of COVID-19 emerged as a prominent and recurring theme of 14 interviews with US-based peer educators. Using a qualitative content analysis approach supported by WebQDA (Qualitative Data Analysis Software) we identified key themes and subthemes in the interview data. Findings Major themes included the impact of COVID-19 in the classroom and institution and also in the personal lives of the students and educators. A prominent subtheme was the introduction, or shift, in technology. Technology was seen as a powerful tool for expanding educational opportunities for older adults and engaging a broader audience. The resilience and adaptability of educators in navigating the challenges posed by the pandemic were also frequently discussed. Conclusion This study highlights the way in which the COVID-19 pandemic has reshaped education within Lifelong Learning Institutes. The findings underscore the importance of addressing the specific needs and challenges faced by older adult peer educators and highlight the opportunities for enhancing specific person-based support and allocating resources effectively in the evolving post-pandemic educational landscape.

Unveiling the determinant factors of go club program loyalty in a super application

This research aimed to analyze the factors affecting super application brand loyalty with the emergence of digital loyalty programs in Indonesia. A strong super-application has gained popularity and sustained customer demand due to Indonesia's smartphone industry expansion. To achieve this objective, a quantitative-qualitative approach utilizing Structured Equation Modeling (SEM) and interviews was employed. Ten-fold indicators determined the sample size, while convenience sampling gathered 392 respondents. The software for data analysis is SmartPLS 4.1.0.0. The results indicate the acceptance of eight hypotheses and the rejection of the remaining two. Playfulness is positively affecting attitudes and loyalty program usage. Next, affective commitment positively affects attitude and loyalty program usage. Next, reward attractiveness positively affects attitude. Finally, the relationship between attitude, usage, satisfaction, and loyalty is significant. The determinant factors of brand loyalty were playfulness, reward attractiveness, and affective commitment. The theoretical contribution is that the impact of loyalty programs on brand loyalty reveals that factors like playfulness and affective commitment influence attitudes and usage, despite gamification's potential moderating effect. The practical implication is that Gojek should improve its loyalty program strategy by diversifying the range of rewards and integrating gamification, particularly in-app games, to maintain user engagement and brand loyalty.