Research Questions Research Articles

Investigating students’ perceptions of self-directed learning in mathematics at the basic school level

Self-directed learning (SDL) is an educational approach, where individuals take initiative and responsibility for their own learning, choosing what, how, and when to learn. It is assumed that SDL empowers students to tailor their educational experiences to personal interests and pace of learning, fostering autonomy and lifelong learning habits. This study aims to investigate on students’ attitudes toward SDL in mathematics at basic school level using quantitative research design (survey). The data were collected from students using SDL attitude scale. Total papulation of the survey consists of students from six publics and eight private schools, out of which students from two purposefully selected schools from each category for the sample. From the four school, 120 students were selected. Attitudes towards SDL in mathematics scale developed by the researchers were applied to collect the data and were analyzed using SPSS version26 based on the research questions. The study revealed that students had negative attitude toward self-management, self-monitoring and self-motivation as required for SDL in mathematics at basic level school. Moreover, there is no significant difference in student’s attitudes toward SDL in mathematics between public and private school, and between male and female students. The result shows that student’s attitude towards SDL changes positively as they participated in different teaching learning techniques applied for engaging them in learning such as KWL, jigsaw, reciprocal teaching, think-share-pair, and cooperative, and collaborative instruction. This implies that negative attitude towards a pedagogical approach does not mean that students pay less attention to the approach but changes when applied appropriately.<i> </i>

The experiences of gender and sexually diverse parents using support and services for their young children: An integrative review.

To address: What are the experiences of 2SLGBTQQIA+ parents using parenting supports and services to meet their children's early childhood development needs (<5 years of age)? Whittemore and Knafl's (2005) integrative review methodology. Electronic databases were searched from 2000 to October 14, 2022 for empirical studies or reviews addressing the research question. The title and abstract of 12,158 articles were screened for inclusion in the review by two independent researchers; 175 of these articles underwent full-text review. Studies selected were critically appraised using a Joanna Briggs Institute Critical Appraisal tool. Relevant key findings were extracted from each study and entered into N-VIVO-12. Thematic content analysis was employed and PRISMA guidelines were adhered to. A total of 18 articles (15 qualitative and three multi-method studies) met the inclusion criteria and were selected for the review. Seven themes were revealed from analysis of the studies: (1) 2SLGBTQQIA+ Status kept a secret; (2) Forced to come out; (3) Heteronormative messaging; (4) Feeling excluded; (5) Stigmatised; (6) Parents act as educators; and (7) Positive experiences. This integrative review provides nurses with insight into the experiences of 2SLGBTQQIA+ parents using health care services for their young child. This article highlights what changes nurses need to make to their practice to ensure appropriate, inclusive care for clients of diverse sexual and gender identities and their families. Health care providers, especially nurses, have an opportunity to improve the experiences of these families and positively impact their health and well-being. Additionally, there is a need for research with the 2SLGBTQQIA+ parent community and the use of rigorous methodological techniques, including clearly linking participants' gender and sexual identities with study findings, to improve our understanding of 2SLGBTQQIA+ parent experiences. Although there was no direct patient contribution to the work since it was an integrative review of the literature, indirectly patient contributions are incorporated from the original research results of studies incorporated into this review.

Interventions to improve nurse-family communication in the emergency department: A scoping review.

To determine and describe what interventions exist to improve nurse-family communication during the waiting period of an emergency department visit. Communication between nurses and families is an area needing improvement. Good communication can improve patient outcomes, satisfaction with care and decrease patient and family anxiety. Scoping Review. A scoping review was conducted following the Joanna Briggs Institution methodology: (1) identify the research question, (2) define the inclusion criteria, (3) use a search strategy to identify relevant studies using a three-step approach, (4) select studies using a team approach, (5) data extraction, (6) data analysis, and (7) presentation of results. Medline, CINAHL, EMBASE, PsychInfo and grey literature were searched on 3 August 2022. The search yielded 1771 articles from the databases, of which 20 were included. An additional seven articles were included from the grey literature. Paediatric and adult interventions were found targeting staff and family of which the general recommendations were summarised into communication models. Future research should focus on evaluating the effectiveness of interventions using a standardised scale, understanding the specific needs of families, and exploring the communication models developed in this review. Communication models for triage nurses and all emergency department nurses were developed. These may guide nurses to improve their communication which will contribute to improving family satisfaction. PRISMA-ScR. Protocol has been registered with the Open Science Framework, registration number 10.17605/OSF.IO/ETSYB. No patient or public contribution.

The Impact of External Financial Factors on the Effect of Ecological Innovation on Small and Medium-Sized Businesses Performance, Capacity for the Sustainability Economy

Eco-innovation has been viewed as a tool for protecting the environment since the 1990s. The impact of external financial factors on eco-innovation is expected to reduce waste, air pollution, and the use of materials and resources. However, the effect of eco-innovation on economic and environmental performance has received little attention. More specifically, since the 1990s, eco-innovation has begun to be viewed as a tool to protect the environment. Eco-innovations are expected to reduce waste, air pollution, and the use of materials and resources. However, the impact of eco-innovation on financial and environmental performance has received little attention. Small and medium-sized enterprises (SMEs) can function better, thanks in large part to the social fabric of the organization. In addition, there is a positive correlation between eco-innovation and GSCM, and these concepts significantly affect the development of CE capabilities. Our last conjecture shows a critical link between GSCM and CE skills. Our findings suggest that relationships between ideas for overarching design are more difficult to establish through more direct or indirect management of multiple tiers of suppliers and that major companies are expanding in ways that are likely to promote sustainability while bringing in sizeable New research questions.

Writing with artificial intelligence? Ad-hoc-survey findings raise awareness for critical literacy at the International Literacy Day

ABSTRACT The International Literacy Day 2023 was the first after the launch the text generating artificial intelligence ChatGPT. This was the reason for a Literacy Promptathon that allows users to learn how to handle text and image generation. The International Literacy Day media coverage for the first time touched on the question of AI generated text. One of the difficulties of generative AI is that it does not necessarily produce true information. However, findings show that critical literacy is most difficult for adults with low literacy performance. Research shows that disinformation, algorithmic biases and data collection are among the most relevant dangers of generative AI, while controlling these dangers requires precise reading and builds on high literacy skills. The research question was how users handle the risk of disinformation and whether this varies by experience. Data collection used an ad-hoc online survey with 202 respondents. Findings indicate an overall high awareness to avoid fake news, but not necessarily adequate fact-checking habits, especially among less experienced AI users. While limited to the respondents, the findings give reason to assume that there is a substantial need to improve competences in fact-checking. Adult and continuing education have a pioneering role in this regard.

Dog Foster Volunteer Retention

ABSTRACT Volunteers that provide foster care in their homes are a critical resource for animal shelters and rescues and make significant contributions to animal welfare. This project explores the support needs identified by canine foster volunteers via a national survey of 611 respondents by answering the following three research questions: 1. Why do foster volunteers consider ending their foster service? 2. What types of training and support do dog fosters want and need from their organizations? 3. Do the types of support needed vary based on the nature of the dog fostered (those with medical versus behavioral challenges)? Based on descriptive, content, and correlation analysis the findings suggest that foster volunteers are generally satisfied with the support provided by their organizations. However, they are less positive about the extent of communication between volunteers and their organization, the amount of training they have received beyond the basics, and the social supports that would accrue from a foster volunteer network, areas that extant research suggests are important for volunteer satisfaction and retention.

A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security

IIoT “Industrial Internet of Things” refers to a subset of Internet of Things technology designed for industrial processes and industrial environments. IIoT aims to make manufacturing facilities, energy systems, transportation networks, and other industrial systems smarter, more efficient and connected. IIoT aims to reduce costs, increase productivity, and support more sustainable operations by making industrial processes more efficient. In this context, the use of IIoT is increasing in production, energy, healthcare, transportation, and other sectors. IoT has become one of the fastest-growing and expanding areas in the history of information technology. Billions of devices communicate with the Internet of Things with almost no human intervention. IIoT consists of sophisticated analysis and processing structures that handle data generated by internet-connected machines. IIoT devices vary from sensors to complex industrial robots. Security measures such as patch management, access control, network monitoring, authentication, service isolation, encryption, unauthorized entry detection, and application security are implemented for IIoT networks and devices. However, these methods inherently contain security vulnerabilities. As deep learning (DL) and machine learning (ML) models have significantly advanced in recent years, they have also begun to be employed in advanced security methods for IoT systems. The primary objective of this systematic survey is to address research questions by discussing the advantages and disadvantages of DL and ML algorithms used in IoT security. The purpose and details of the models, dataset characteristics, performance measures, and approaches they are compared to are covered. In the final section, the shortcomings of the reviewed manuscripts are identified, and open issues in the literature are discussed.

Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents

From science to law enforcement, many research questions are answerable only by poring over a large amount of unstructured text documents. While people can extract information from such documents with high accuracy, this is often too time-consuming to be practical. On the other hand, automated approaches produce nearly-immediate results, but are not reliable enough for applications where near-perfect precision is essential. Motivated by two use cases from criminal justice, we consider the benefits and drawbacks of various human-only, human–machine, and machine-only approaches. Finding no tool well suited for our use cases, we develop a human-in-the-loop method for fast but accurate extraction of structured data from unstructured text. The tool is based on automated extraction followed by human validation, and is particularly useful in cases where purely manual extraction is not practical. Testing on three criminal justice datasets, we find that the combination of the computer speed and human understanding yields precision comparable to manual annotation while requiring only a fraction of time, and significantly outperforms the precision of all fully automated baselines.

Deep learning with noisy labels in medical prediction problems: a scoping review.

Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical." A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

Grand rounds in methodology: designing for integration in mixed methods research

Mixed methods research is a popular approach used to understand persistent and complex problems related to quality and safety, such as reasons why interventions are not implemented as intended or...

A review on deep learning-based object tracking methods

A deep learning algorithm tracks an object’s movement during object tracking and the main challenge in the tracking of objects is to estimate or forecast the locations and other pertinent details of moving objects in a video. Typically, object tracking entails the process of object detection. In computer vision applications the detection, classification, and tracking of objects play a vital role, and gaining information about the various techniques available also provides significance. In this research, a systematic literature review of the object detection techniques is performed by analyzing, summarizing, and examining the existing works available. Various state of art works are collected from standard journals and the methods available, cons, and pros along with challenges are determined based on this the research questions are also formulated. Overall, around 50 research articles are collected, and the evaluation based on various metrics shows that most of the literary works used Deep convolutional neural networks (Deep CNN), and while tracking the objects object detection helps in enhancing the performance of these networks. The important issues that need to be resolved are also discussed in this research, which helps in leveling up the object-tracking techniques.

Computational positive psychology: advancing the science of wellbeing in the digital era

ABSTRACT Positive psychology (PP) is in its third wave, evolving towards an interdisciplinary study of wellbeing. This article proposes computational positive psychology (CPP) as an emerging interdisciplinary field that integrates PP with computational science and technology to advance the understanding of wellbeing and develop evidence-based strategies to promote sustainable flourishing. The key features of CPP include addressing novel research questions that have received limited investigation in the existing literature, collecting large-scale and multimodal data, constructing computational models, and utilizing the results to advance theories, research, and the practice of wellbeing. We review CPP research topics such as: (1) advancing theoretical wellbeing models, (2) efficiently measuring wellbeing and positive traits, (3) exploring interdependent dynamics between individual and systemic wellbeing, (4) enhancing positive psychology interventions, and (5) improving collective wellbeing. The transformative potential of CPP for understanding and promoting wellbeing in the digital era is highlighted, and future research directions are discussed.

Pengelolaan Kampung Nelayan Sebagai Desa Wisata di Kabupaten Majene, Sulawesi Barat

This research aims at 1) analyzing the tourism potential of Kampung Nelayan (Majene Regency) as tourism village; 2) recommending steps or mechanisms to implement Kampung Nelayan as tourism village. This paper employs a qualitative approach to answer research questions through research visit in July and September 2019. The authors collected research data through in-depth interviews and direct observation. The research reveals that Kampung Nelayan has the potential for tourism village based on community empowerment principles. Kampung Nelayan has potential of marine tourism and cultural tourism. The steps to implement Kampung Nelayan as tourism village including 1) identification of members of community under the categories of internal and external community groups; 2) involving the community members in tourism programs and 3) follow up of community collaboration by the internal and external groups of community.

Availability and stability of palliative care for family members of terminally ill patients in an integrated model of health and social care

BackgroundPalliative care and the integration of health and social care have gradually become the key direction of development to address the aging of the population and the growing burden of multimorbidity at the end of life in the elderly.AimsTo explore the benefits/effectiveness of the availability and stability of palliative care for family members of terminally ill patients in an integrated institution for health and social care.MethodsThis prospective observational study was conducted at an integrated institution for health and social care. 230 patients with terminal illness who received palliative care and their family members were included. Questionnaires and scales were administered to the family members of patients during the palliative care process, including quality-of-life (SF-8), family burden (FBSD, CBI), anxiety (HAMA), and distress (DT). We used paired t-tests and correlation analyses to analyze the data pertaining to our research questions.ResultsIn the integrated institution for health and social care, palliative care can effectively improve quality of life, reduce the family’s burden and relieve psychological impact for family members of terminally ill patients. Palliative care was an independent influencing factor on the quality of life, family burden, and psychosocial status. Independently of patient-related and family-related factors, the results are stable and widely applicable.ConclusionThe findings underline the availability and stability of palliative care and the popularization of an integrated service model of health and social care for elder adults.

Revisiting Bundle Recommendation for Intent-aware Product Bundling

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.

Comparing Core Concepts of Child Protection: Reflecting the Social Context of Families

ABSTRACT In this paper, we utilize both English and German language scholarly literature to describe the use of four core concepts in child protection. Using the scholarly literature, we address the research questions: (1) How are these four concepts used in regard to child protection services? (2) How do these four concepts address and reflect the social circumstances of families? Expansion to just one other language adds important insight to our understanding and might serve to guide further scholarship expansion to other languages. Inclusion of Kindeswohlgefährdung (Endangerment of the best interest of the child) as a specific concept in the German language environment facilitates this bi-lingual approach.

Effective Parameters for Orthodontic Tooth Movement Acceleration with Photobiomodulation: An Umbrella Review.

Objective: To answer this research question: What are the effective wavelength, power, and energy density parameters for achieving dental movement acceleration? Background Data: Photobiomodulation (PBM) has been clinically studied for its ability to accelerate dental movements in orthodontics. However, its effectiveness is dose dependent. Methods: The search was carried out in PubMed, SCOPUS, and ISI Web of Science. The quality of the included systematic reviews was performed using the AMSTAR 2 tool. The risk of bias was assessed using the ROBIS tool. Results: In total, 29 articles in PubMed, 75 in Scopus, and 61 in ISI Web of Science. Finally, only five systematic reviews were included. Conclusions: The results showed the range from 730 to 830 nm as the most effective range of wavelength to accelerate the orthodontic dental movement. A power range of 0.25-200 mW, with emphasis on the direct correlation between power, wavelength, and energy density. Energy density has not been adequately reported in the most randomized controlled clinical trials.

Trends in prevalence of arthritis by race among adults in the United States, 2011–2018

BackgroundThere is currently a lack of comprehensive prevalence information on arthritis and its various classifications among adults in the U.S., particularly given the notable absence of detailed data regarding the Asian population. We examined the trends in the prevalence of arthritis, including osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis (PsA), and other types of arthritis, among U.S. adults by race between 2011 and 2018.MethodsWe analyzed data from the National Health and Nutrition Examination Survey (NHANES), spanning from 2011 to 2018. Our study focused on a nationally representative sample of U.S. adults aged 20 and older. Participants who answered “y es” to the research question “Doctors ever said you had arthritis?” were classified as having arthritis. Further classification into specific diseases was based on responses to the question “Which type of arthritis was it?” with options including “OA or degenerative arthritis, ” “RA, ” “PsA, ” or “Other. ”ResultsWe analyzed 22,566 participants from NHANES (2011–2018), averaging 44.8 years, including 10,927 males. The overall arthritis prevalence rose significantly from 22.98% (95% CI: 21.47–24.55%) in 2011–12 to 27.95% (95% CI: 26.20–29.76%) in 2017–18 (P for trend < 0.001). OA increased from 12.02% (95% CI: 10.82–13.35%) in 2011 to 14.93% (95% CI: 13.47–16.51%) in 2018 (P for trend < 0.001). RA and PsA remained stable (P for trend = 0.220 and 0.849, respectively), while other arthritis rose from 2.03% (95% CI: 1.54–2.67%) in 2011–12 to 3.14% (95% CI: 2.56–3.86%) in 2017–18 (P for trend = 0.001). In Whites, Asians, and other races , arthritis and RA prevalence increased significantly (P for trend < 0.05). OA and other arthritis rose in Whites and other races (P for trend < 0.05), but no significant change occurred in the black population. The prevalence of PsA remained stable across all racial groups, with no statistically significant changes.ConclusionsIn this nationally representative U.S. adult survey spanning 2011 to 2018, we identified a rising prevalence trend in arthritis, OA, and other arthritis, with notable variations among different racial groups.

Methodological identification of anomalies episodes in ECG streams: a systematic mapping study

An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia. Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave’s classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase’s process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.

Motives of Parents Who Request the Catholic Sacraments of Initiation for Their Children

ABSTRACT Using attachment theory to interrogate findings, a single-case study investigated why parents present their children for the Sacraments of Initiation into the Catholic Church. Twelve parents from the Illawarra region, Australia, participated in the study. Robert K. Yin’s (Case Study Research and Applications: Design and Methods. 6th ed. Sage, 2018) analytic strategy resulted in two research questions (RQ), namely, why do parents present their children for Baptism and secondly, why do they continue with the sacraments of First Holy Communion and Confirmation despite having little to no Church contact during the in-between years? Five overarching themes were found for the first RQ, including non-religious incentive, focus on the child, struggle in understanding Baptism, future orientation and cultural expectation. The second RQ revealed four overarching themes, namely, child-focused apprehension, child-focused commitment, expenditure of time and priorities in a busy life. Emphasis on the child’s welfare was evident in all themes. Discussion focuses on the conflict between Church commitment and emphasis on the child’s well-being.