Data Collection Phase Research Articles

An Investigation of the Intercultural Sensitivity Levels of Summer Work and Travel Programme Students

A variety of programmes and schemes make travel more affordable and attractive for young people. They provide opportunities to experience intercultural communication first-hand to those who could not have imagined without being involved in such institutionalized schemes. One such programme is Work & Travel which is offered in the USA. Young people who gain hands-on experience in the USA can potentially gain intercultural skills. A few studies have focused on to what extent this experience could live up to the expectations (1 reference). There is a lack of research on the impact of Summer Work and Travel (SWT) participants and the factors affecting their levels of intercultural sensitivity, especially in the Turkish context. Therefore, the current study aims to measure the intercultural sensitivity (IS) of the SWT participants from Türkiye and the factors which impact their level of IS. The sequential mixed method was used, and the quantitative data was first gathered via a five-point Likert scale with 129 participants. Semi-structured interviews with ten volunteering participants followed the quantitative data collection phase. It was concluded from the data analysis that the participants in the SWT experience had a high level of IS. While some participants had less confidence due to their insufficient language proficiency, they reported that they became more confident as they improved their language skills and adapted to the intercultural environment. The researchers also concluded that the high IS might have resulted from the participants’ high level of daily interaction with people from different cultures and at the social events they attended.

Information and Communication Technology for Managing Social Isolation and Loneliness Among People Living With Parkinson Disease: Qualitative Study of Barriers and Facilitators.

Parkinson disease (PD) is a complex, noncurable, and progressive neurological disease affecting different areas of the human nervous system. PD is associated with both motor and nonmotor symptoms, which negatively affect patients' quality of life and may cause changes in socialization such as intentional social withdrawal. This may further lead to social isolation and loneliness. The use of information and communication technology (ICT) plays an important role in managing social isolation and loneliness. Currently, there is a lack of research focusing on designing and developing ICT solutions that specifically address social isolation and loneliness among people living with PD. This study addresses this gap by investigating barriers and social needs in the context of social isolation, loneliness, and technology use among people living with PD. The insights gained can inform the development of effective ICT solutions, which can address social isolation and loneliness and improve the quality of life for people living with PD. A qualitative study with 2 phases of data collection were conducted. During the first phase, 9 health care professionals and 16 people living with PD were interviewed to understand how PD affects social life and technology use. During the second phase, 2 focus groups were conducted with 4 people living with PD in each group to gather insights into their needs and identify ways to manage social isolation and loneliness. Thematic analysis was used to analyze both data sets and identify key themes. The results showed that the barriers experienced by people living with PD due to PD such as "fatigue," "psychological conditions," "social stigma," and "medication side effects" affect their social life. People living with PD also experience difficulties using a keyboard and mouse, remembering passwords, and navigating complex applications due to their PD-related physical and cognitive limitations. To manage their social isolation and loneliness, people living with PD suggested having a simple and easy-to-use solution, allowing them to participate in a digital community based on their interests, communicate with others, and receive recommendations for social events. The new ICT solutions focusing on social isolation and loneliness among people living with PD should consider the barriers restricting user's social activities and technology use. Given the wide range of needs and barriers experienced by people living with PD, it is more suitable to adopt user-centered design approaches that emphasize the active participation of end users in the design process. Importantly, any ICT solution designed for people living with PD should not encourage internet addiction, which will further contribute to the person's withdrawal from society.

Modeling climate change adaptation for sustainable coastal zones using GIS and AHP

The world is currently confronting one of its biggest environmental challenges: combating climate change. Coastal zones are one of the areas thought to be most sensitive to current and future climate change threats. The paper integrates Remote Sensing (RS), Geographic Information System (GIS) techniques, and Multi-Criteria Decision Analysis (MCDA) to detect vulnerable areas from climate change impacts in coastal zones in order to recommend adaptation systems in new coastal zones that can withstand various climatic changes. The proposed decision-making framework was developed in three phases: 1) climate data collection and processing; 2) Coastal Climate Impact Assessment (CCIA) model development; and 3) implementation and adaptation system selection. The climate data collection and processing phase involves determining the most significant climate change parameters and their indicators that affect coastal zone stability, extracting climatic data indicators from different climate database sources, and prioritizing the selected indicators. The indicators’ weights were estimated using the Analytical Hierarchy Process (AHP) through a questionnaire survey shared with experts in climate change impacts. A CCIA model development phase involves the formulation of the proposed model using GIS technique to discover the vulnerable areas according to the most dominant impact. The implementation and adaptation system selection phase involves the application of the framework to Al-Alamein New City in Egypt. A sensitivity analysis was conducted to measure the behavior of several climate change parameters to identify the most critical parameter for climate change in Al-Alamein New City. The results showed that the geology of the region is the most crucial component influenced by climate change. It is capable of producing a very sensitive area in the coastal zone while also taking other factors into account. When creating new urban neighborhoods, the erosion of the shoreline is the least important factor to consider. This is because coastal deterioration is caused by both the influence of metrological data on the region and the impact of human activity. Shoreline deterioration will be reduced if climate conditions are maintained while limiting the impact of human activities. To adapt to the long-term effects of climate change on coastal zones, a combination of soft and hard protection systems should be considered.

Managing the challenge of fatigue for pilots operating ultra-long range flights

IntroductionUltra-long range (ULR) flights are defined as exceeding regulatory limits: normally 16 h flight time. They pose challenges due to long duty periods that could result in extended wakefulness and sleep loss, increasing the risk of fatigue. This study describes the mitigations used to manage fatigue in these operations. Two data collection phases were conducted on the Auckland-Chicago ULR route: when the route commenced (Study 1) and when the flight crew complement was altered (Study 2). Seasonal differences were also investigated.MethodsStudy 1 involved 72 crew who completed diaries and wore an actigraph to record sleep pre-departure, throughout the trip, and on return. In-flight, fatigue, sleepiness and workload were reported, and reaction time performance was measured. Study 2 involved 75 crew and data collection in the northern summer and northern winter. Crew completed diaries throughout the trip.ResultsStudy 1 data found crew sleep longer than usual in the 24 h pre trip and post flights. On the shorter outbound flight in-flight sleep averaged 3.3 h and on the longer inbound flight, 3.3–3.8 h, with most crew taking 3 breaks. Ratings of sleepiness and fatigue increased, and reaction time performance declined across flights, with greater decrements on longer inbound flights. Pilots did not fully adjust their sleep patterns to local time during the layover and no seasonal differences were found. Comparisons between Study 1 and 2 showed no difference in ratings of fatigue and sleepiness or in-flight sleep duration with an altered crew complement. There was a trend for Captains to report greater workload and less in-flight sleep in Study 2.DiscussionMitigations that allow for preparation and recovery are well utilized by crew. In-flight sleep is relatively short and ways of increasing the amount of sleep obtained should be considered. The incomplete adaptation of sleep during the layover has implications for rest break strategies on the return flight. The altered crew complement did not result in higher levels of fatigue or sleepiness or less in-flight sleep on average, although findings suggest a need to understand the effects of changing the crew complement on workload and in-flight sleep for Captains.

Examining dependencies among different time scales in episodic memory - an experience sampling study.

We re-examined whether different time scales such as week, day of week, and hour of day are independently used during memory retrieval as has been previously argued (i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity. We also used pointwise mutual information to directly calculate the degree of dependency between time scales in a formal way. Participants were provided with a smartphone and were asked to wear it around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (i.e., week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life.

Ankyloglossia and its Importance in Sustained Breastfeeding

Ankyloglossia, commonly known as tongue-tie, is reported to affect 7% of infants and can impact ability to breastfeed. A comprehensive review of literature was taken which included an assessment of which diagnostic tools are most accurate in determining ankyloglossia associated with difficult breastfeeding. It was noted that there was not a standardized approachto identifying and treating ankyloglossia in breastfed infants. Based on this review, a multidisciplinary team was established to develop a team for a holistic assessment of breastfeeding efficacy. A protocol was developed for assessment of frenotomy impact on breastfeeding. It included LATCH, Coryllos Scale, and IBFAT as the most useful evaluation tools and developed a prospective assessment in infants with ankyloglossia. This study is in the early data collection phase. The goal of this study is to fill an important gap in the knowledge of the impact of ankyloglossia in sustained breastfeeding.

Spousal support, parent-nurse partnership and caregiver burden among parents of children with chronic diseases: A cross-sectional study.

To examine the effects of spousal support and parent-nurse partnership on caregiver burden of parents of children with chronic disease. With the trend of increasing the global number of children with chronic diseases, the parental caregiver burden has become increasingly prevalent. Cross-sectional study. The study participants included 115 parents of children diagnosed with chronic disease at a general hospital in South Korea. The study duration was 4 June 2021-30 April 2022. Self-reported measures included the parent-nurse partnership scale, the Korean version of the Parenting Alliance Inventory and the family caregiver burden scale. T-tests, ANOVA, Pearson's correlation coefficients and hierarchical linear multiple regression were conducted using IBM SPSS version 26.0. This study followed STROBE guideline. Parental caregiver burden was significantly negatively associated with spousal support and parent-nurse partnership. Factors significantly influencing caregiver burden were parental alcohol consumption; child's inherited metabolic disease, cardiovascular disease, disease relating to haematological tumours or kidney disease diagnosis; child's health perceived as poor by parents; child's dependency perceived as high by parents; hospitalization recency; and low spousal support. These factors accounted for 65% of caregiver burden. Parental caregiver burden was related to spousal support and parent-nurse partnership, but the primary factor affecting caregiver burden was spousal support. The results highlighted the role of healthcare professionals in educating parents of children with chronic diseases to facilitate spousal support and have implications for nursing and community-based interventions to reduce parental caregiver burden. Furthermore, they underlined that policymakers and other stakeholders should pay attention to the parental caregiver burden through government-based, family-centered strategies. Parents of children with chronic disease were recruited to perform the self-administered survey in the phase of data collection.

Living with multimorbidity: A qualitative exploration of shared experiences of patients, family caregivers, and healthcare professionals in managing symptoms in the United States.

To elicit experiences of patients, family caregivers, and healthcare professionals in intermediate care units (IMCUs) in an academic medical centre in Baltimore, MD related to the challenges and intricacies of multimorbidity management to inform development of a multimorbidity symptom management toolkit. Experience-based co-design. Between July and October 2021, patients aged 55 years and older with multimorbidity admitted to IMCUs at an academic medical centre in Baltimore, Maryland, USA were recruited and interviewed in person. Interdisciplinary healthcare professionals working in the IMCU were interviewed virtually. Participants were asked questions about their role in recognizing and treating symptoms, factors affecting the quality of life, symptom burden and trajectory over time, and strategies that have and have not worked for managing symptoms. An inductive thematic analysis approach was used for analysis. Twenty-three interviews were conducted: 9 patients, 2 family caregivers, and 12 healthcare professionals. Patients' mean age was 67.5 (±6.5) years, over half (n = 5) were Black or Hispanic, and the average number of comorbidities was 3.67. Five major themes that affect symptom management emerged: (1) the patient-provider relationship; (2) open and honest communication; (3) accessibility of resources during hospitalization and at discharge; (4) caregiver support, training, and education; and (5) care coordination and follow-up care. Patients, caregivers, and healthcare professionals often have similar goals but different priorities for multimorbidity management. It is imperative to identify shared priorities and target holistic interventions that consider patient and caregiver experiences to improve outcomes. This paper addresses the paucity of research related to the shared experience of disease trajectory and symptom management for people living with multimorbidity. We found that patients, caregivers, and healthcare professionals often have similar goals but different care and communication priorities. Understanding differing priorities will help better design interventions to support symptom management so people with multimorbidity can have the best possible quality of life. We have adhered to the Consolidated Criteria for Reporting Qualitative Studies (COREQ) guidelines in our reporting. This study has been designed and implemented with patient and public involvement throughout the process, including community advisory board engagement in the project proposal phase and interview guide development, and member checking in the data collection and analysis phases. The method we chose, experience-based co-design, emphasizes the importance of engaging members of a community to act as experts in their own life challenges. In the coming phases of the study, the public will be involved in developing and testing a new intervention, informed by these qualitative interviews and co-design events, to support symptom management for people with multimorbidity.

Holding on to life: COVID-19 pandemic impact, adaptation and resilience in Abeokuta, Nigeria

The COVID-19 posed severe public health effects across the world to an extent that it was declared a pandemic. Aside the health effects are other consequences associated with measures such as lockdown, isolation and physical distancing to curb the spread of the virus. This study assesses the impact of the pandemic on households and their adaptation across different residential density zones in Abeokuta, Nigeria, with a novelty of understanding its socio-spatial context. This study utilised a quantitative research design based on a cross-sectional survey for the phases of data collection, analysis and interpretation. A two-stage sampling technique was employed in selecting 422 participants for the study. All participants were administered questionnaire and 391 copies were considered during data analysis. The data were analysed using cross-tabulation and the hypotheses tested using ANOVA. Findings on socioeconomic characteristics showed that the distribution of respondents descriptively varied across the high, medi residential density zones. Evidence of significant variances in the impact of COVID-19 pandemic on households ([F (2, 388) = 30.406, p = 0.000]) and their adaptation strategies ([F (2, 388) = 39.539, p = 0.000]) were established across residential zones in Abeokuta. The practical implication provides for policymakers and stakeholders to design targeted interventions and support measures for households. The theoretical implication is about contributing to the existing body of knowledge on the impact of pandemics on households by establishing a socio-spatial context and highlighting the need for socioeconomic context-specific responses.

Student participation in teachers’ action research: teachers’ and students’ engagement in social learning

ABSTRACT In the literature on professional learning communities, students are rarely considered participants. Research indicates that student participation is primarily designated as a ‘data source’, with students having limited involvement in decision making. However, some studies suggest that an agentic approach to teachers’ professional development (such as action research) may foster student agency. This article aims at deepening the knowledge of student participation in teachers’ action research. Drawing on data from 14 teachers’ action research in a Swedish upper secondary school, the findings show that student participation occurs most in the data collection phase, intended to obtain students’ experiences with classroom interventions. Employing a social learning framework, the extent of student participation is suggested as involving issues of accountability and teachers’ prior learning experiences. This article argues that while teachers are responsible for both the classroom instruction and the development work, viewing students as partners in social learning offers opportunities for increased student participation in the development of classroom practices.

Investigating top-down and bottom-up strategic alignment of event leveraging outcomes: the case of the 2021 UCI Road World Championships

ABSTRACT Research question It is generally agreed upon that deliberate planning is needed to achieve pre-determined positive outcomes from sport events (i.e. event leveraging). There is less consensus around the specific strategies that should be used to achieve such outcomes, and ownership of such strategies. A largely conceptual suggestion has been made that both top-down and bottom-up stakeholders should be involved in event leveraging. Therefore, the purpose of this paper is to investigate the (mis)alignment of top-down and bottom-up stakeholders’ event leveraging objectives and how this (mis)alignment relates to objective achievement. Research methods In the context of the city of Leuven (Belgium), and the 2021 UCI Road World Championships, a case study methodology was employed with three phases of data collection and analysis of (1) top-down stakeholder documents; (2) semi-structured interviews with bottom-up stakeholders (n = 8); and (3) online questionnaires with residents (n = 3662). Results and findings We found alignment for only one top-down and bottom-up objective (i.e. promote cycling as a means of active transportation), which was found to be achieved through examining residents’ use of cycling for groceries. The remaining objectives were not aligned, and therefore were not fully met or sustained as indicated through resident opinion and behaviour. Implications The findings provide empirical support for previous conceptual notions that both top-down and bottom-up strategies to event leveraging are needed. Future research can help support leveraging sport events by working with both top-down and bottom-up stakeholders prior to hosting to help facilitate objective alignment, and foster relationships to maximize outcomes.

A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education.

As the field of artificial intelligence (AI) continues to progress, the use of AI-powered chatbots, such as ChatGPT, in higher education settings has gained significant attention. This paper addresses a well-defined problem pertaining to the critical need for a comprehensive examination of students' ChatGPT adoption in higher education. To examine such adoption, it is imperative to focus on measuring actual user behavior. While measuring students' ChatGPT usage behavior at a specific point in time can be valuable, a more holistic approach is necessary to understand the temporal dynamics of AI adoption. To address this need, a longitudinal survey was conducted, examining how students' ChatGPT usage behavior changes over time among students, and unveiling the drivers of such behavior change. The empirical examination of 222 Dutch higher education students revealed a significant decline in students' ChatGPT usage behavior over an 8 month period. This period was defined by two distinct data collection phases: the initial phase (T1) and a follow-up phase conducted 8 months later (T2). Furthermore, the results demonstrate that changes in trust, emotional creepiness, and Perceived Behavioral Control significantly predicted the observed change in usage behavior. The findings of this research carry significant academic and managerial implications, as they advance our comprehension of the temporal aspects of AI adoption in higher education. The findings also provide actionable guidance for AI developers and educational institutions seeking to optimize student engagement with AI technologies.

Computational analysis of dystopian elements in the partition fiction: A machine learning approach to the indian English novels

Despite the growth of digital humanities, the specific application of computational methods to analyse literary themes and elements remains underexplored. This study aims to use machine learning algorithms for text classification on seven novels about the partition of India. The article has a dual objective: firstly, to develop and implement an innovative opinion mining framework that leverages machine learning techniques to identify and classify dystopian elements in Partition novels; and secondly, to evaluate and compare the efficacy of the model in accurately classifying dystopian sentiments within selected literary texts. The proposed framework includes six phases of data collection, test-pre-processing, text extraction, text exploration, modelling or classification using machine leaning, and performance evaluation. Machine learning approaches such as Logistic Regression, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor are adopted to classify the text based on compiled dictionary. First, the study revealed that the main components and characteristics of dystopian elements found in selected narratives are ‘fear’, ‘suffer’, ‘oppression’ and ‘violence’. Second, the analysis showed that SVM with CountVectorizer, using imbalanced dataset and random oversampling dataset, outperforms other classifiers in classifying dystopian types in the selected novels. The results also suggest that CountVectorizer works better for the dataset compared to TF-IDF.

Evaluation of Ambient Sensor Systems for the Early Detection of Heart Failure Decompensation in Older Patients Living at Home Alone: Protocol for a Prospective Cohort Study.

The results of telemedicine intervention studies in patients with heart failure (HF) to reduce rehospitalization rate and mortality by early detection of HF decompensation are encouraging. However, the benefits are lower than expected. A possible reason for this could be the fact that vital signs, including blood pressure, heart rate, heart rhythm, and weight changes, may not be ideal indicators of the early stages of HF decompensation but are more sensitive for acute events triggered by ischemic episodes or rhythm disturbances. Preliminary results indicate a potential role of ambient sensor-derived digital biomarkers in this setting. The aim of this study is to identify changes in ambient sensor system-derived digital biomarkers with a high potential for early detection of HF decompensation. This is a prospective interventional cohort study. A total of 24 consecutive patients with HF aged 70 years and older, living alone, and hospitalized for HF decompensation will be included. Physical activity in the apartment and toilet visits are quantified using a commercially available, passive, infrared motion sensing system (DomoHealth SA). Heart rate, respiration rate, and toss-and-turns in bed are recorded by using a commercially available Emfit QS device (Emfit Ltd), which is a contact-free piezoelectric sensor placed under the participant's mattress. Sensor data are visualized on a dedicated dashboard for easy monitoring by health professionals. Digital biomarkers are evaluated for predefined signs of HF decompensation, including particularly decreased physical activity; time spent in bed; increasing numbers of toilet visits at night; and increasing heart rate, respiration rate, and motion in bed at night. When predefined changes in digital biomarkers occur, patients will be called in for clinical evaluation, and N-terminal pro b-type natriuretic peptide measurement (an increase of >30% considered as significant) will be performed. The sensitivity and specificity of the different biomarkers and their combinations for the detection of HF decompensation will be calculated. The study is in the data collection phase. Study recruitment started in February 2024. Data analysis is scheduled to start after all data are collected. As of manuscript submission, 5 patients have been recruited. Results are expected to be published by the end of 2025. The results of this study will add to the current knowledge about opportunities for telemedicine to monitor older patients with HF living at home alone by evaluating the potential of ambient sensor systems for this purpose. Timely recognition of HF decompensation could enable proactive management, potentially reducing health care costs associated with preventable emergency presentations or hospitalizations. ClinicalTrials.gov NCT06126848; https://clinicaltrials.gov/study/NCT06126848. PRR1-10.2196/55953.

Nursing Undergraduate Students' Experiences and Perceptions of Blended Learning in Pediatric Nursing: A Mixed Methods Study.

Blended learning is a teaching approach that integrates online self-learning and classroom teaching. It emphasizes students' self-directed learning, actively engaging, as well as communication and collaboration. Blended learning develops the qualities in nursing undergraduate students with problem-solving ability, clinical thinking, and communication skills as nurses have. Currently, learners' experience has become an important factor in determining the success of blended learning. To learn about the experiences and perceptions of undergraduate nursing students regarding blending learning in pediatric nursing. A mixed methods design beginning with a quantitative data collection and analysis phase to inform the subsequent qualitative phase was utilized. This study was conducted among undergraduate nursing students at one university in China. In the quantitative study phase, a convenience sample of 59 students completed the online survey while the qualitative study phase included nine students recruited by purposive sampling. The qualitative data were collected using individual semistructured in-depth interviews. Audio data were transcribed and subjected to thematic analysis. The findings from those two phases were integrated into the final analysis. The majority (89.8%) of students reported satisfaction with blended learning. Among various teaching methods, case-based learning (CBL) was the most preferred (98.3%). Regarding the qualitative data, students reported positive experiences with blended learning including well-organized course structure, resourceful course content, and high teacher quality. They stated that blended learning improved the learning outcomes such as improving problem-solving ability and developing clinical thinking. They also provided suggestions for achieving better learning outcomes, including providing stressful learning activities, optimizing online course design, and improving the quality of group learning. The blended learning model of pediatric nursing based on the Community of Inquiry framework provides a positive learning experience for nursing undergraduate students. However, ongoing assessment and improvement of the blended learning process are also needed.

Creating Synthetic Datasets for Deep Learning used in Machine Vision

Deep Learning, a subfield of Artificial Intelligence, has enabled a wide range of applications in machine vision, including manufacturing quality control. A critical aspect of Machine Vision using Deep Learning algorithms is the availability of large amounts of visual training data. Collecting training data is time consuming, especially in cases where visual training data is difficult to collect. For example, machine vision systems for object detection in quality inspection need massive datasets with various light, rotation and viewpoints parameters to recognize components. Only the training phases of data collection and annotation are responsible for about 80 % of the time spent. Through advances in synthetically generated training data, the lack of available training data is alleviated and effort for data collection and annotation is reduced. This paper presents an innovative approach to generate synthetic training data using CAD-Tools and rendering software. In this work, synthetic training data is generated using the approach of domain randomization. Domain randomization introduces diversity and variability in training dataset, which has proven to improve the robustness and generalization of deep learning models. The approach is evaluated in a case example. Images of an assembly step are classified in terms of correct or incorrect assembly. The results show that synthetically generated training data used to train deep learning algorithms are fundamentally suitable for Machine Vision. Within this study deep learning models trained with synthetic image reached equally good results as models trained using real-world data. Variation using generated non-realistic image data is able to force neuronal nets to learn features of the object of interest. Synthetically generated training data therefore is an inexpensive source of data avoiding the need to collect and annotate large amounts of training data. Overall, the integration of synthetically generated image data has paved the way for new possibilities in visual application domains.

A randomized controlled add-on trial of fluoxetine and cognitive behavioral therapy for help-seeking men with a sexual interest in children: presentation of the PARACHUTES trial protocol and initial feasibility data.

Sexual Interest in Children (SIC) is a major risk factor for sexual offending, yet clinical trials are sparse. The present protocol outlines a randomized controlled trial (RCT) that aims to investigate the effectiveness of fluoxetine and Cognitive Behavioral Therapy (CBT) in help-seeking men with SIC. Adult men contacting the Swedish telephone helpline PrevenTell are screened for inclusion and invited to further assessment on site. One hundred and eleven men with SIC (defined as DSM-5 pedophilic disorder or hebephilia) will be randomized (1:1:1 ratio) to receive one of three interventions for 14 weeks: (1) an internet-administered psychoeducational program (iPP), (2) iPP and the addition of fluoxetine 20-40 mg or (3) iPP and the addition of internet-administered CBT (iCBT). Exclusion criteria include severe psychiatric illness, contraindicating treatment and an elevated risk of committing hands-on sexual offences. Symptom intensity is assessed at baseline, pre-treatment, every other week for 12 weeks, and post treatment. The primary outcome measure is the Sexual Interest in Children: Current Assessment Scale (SIC: CAS) that quantifies sexual behaviors associated with SIC as well as perceived distress and impairment. Secondary outcomes include measures of dynamic risk-factors for committing sexual offences. The data collected during the initial 20 months of recruitment were analyzed to predict the required number of individuals to be screened and estimate the probable length of the data collection phase. As of March 2022 to November 2023, 146 men have called PrevenTell and disclosed a sexual interest in minors. Following pre-screening, 110 men were excluded from participation in the trial. Current SSRI therapy was the primary reason for exclusion (n = 24; 22%), followed by an elevated risk of committing hands-on sexual offences (n = 14; 13%). Among the 31 men who underwent the screening procedure on site, 26 were allocated to either iPP, iPP+fluoxetine, or iPP+iCBT. The recruitment rate indicates that the trial will be concluded within the pre-estimated timeframe. This is the first RCT of treatment with SSRI and iCBT in a population of help-seeking men with SIC. The significance of this trial and its methodological strengths and limitations are discussed.

A Modelling Framework of an Intelligent News Recommendation Engine for Financial Analysts

Referring to news articles has become a predominant task in the daily routine of each financial analyst, as it helps to make accurate financial insights. However, as there are thousands of news articles generated daily, finding the most relevant articles becomes a time-consuming task. Hence, this study develops a modelling framework of an intelligent news recommendation engine for financial analysts in a particular financial company, which assists in recommending the most appropriate articles according to analysts' preferences in an efficient and effective manner without tedious browsing.In this study, the data collection phase of the recommendation engine is accomplished by retrieving news articles from online news websites using the web scraping technique. The response variable, analysts’ preference level for each article is manually acquired from a group of financial analysts at a selected financial company. In the analysis phase of the recommendation engine, a classification-based method was utilized where three machine learning models, KNN, SVM, Random Forest, and two deep learning models, LSTM and CNN with Natural Language Processing techniques are experimented to discover the best algorithm. Moreover, the synonym replacement method is employed as a text data augmentation method to address the imbalanced problem in the dataset.As CNN obtains the highest accuracy in comparison to other applied methods it is chosen as the most suitable approach in the analysis phase. Moreover, this study reveals that DL models perform considerably higher performances in the context of news recommendation engines rather than ML approaches. Lastly, this framework can be adjustable for any financial company with minor modifications.

An Investigation on Consumer Behavior Concerning “Trendy Drink” in Islamic View

This study was aimed to determine drivers of consumer trust, satisfaction and recommendation to others regarding “Trendy Drink” Mixue Ice Cream Tea. Based on literature review, most of consumer behavior studies were investigated by a single method. Furthermore, research on consumer perspectives concerning food safety in Islamic were still limited. Hence, we extended this study using mixed method. Sampling used is purposive with characteristics Moslem, 17 years old or over and has purchased Mixue Ice Cream and tea in the outlet. Firslty, open-ended interviews were carried out to gather data amplified by second phase of data collection was online survey and the data of 234 participants were analyzed utilizing SEM-PLS. Study revealed that consumers perceived food as safe attributed to its appearance, taste, smell, certification and quality. Additionally, study uncobered that food safety and information credibility were prominent drivers of consumer behavior. This paper explored an underexposed concern on product safety in Islamic view. It contributes to the marketing field empirically, theoretically and practically by extending the implementation of food safety, source of credibility theory, and Maslow`s hierarchy of need theory in the Islamic marketing and food beverages context. Additionally, applying a mixed-method for data analysis broadened the research results` generalisability.

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Intelligent search engines including pre-trained generative transformers (GPT) have revolutionized the user search experience. Several fields including e-commerce, education, and hospitality are increasingly exploring GPT tools to study user reviews and gain critical insights to improve their service quality. However, massive user-review data and imprecise prompt engineering lead to biased, irrelevant, and impersonal search results. In addition, exposing user data to these search engines may pose privacy issues. Motivated by these factors, we present ChatReview, a ChatGPT-enabled natural language processing (NLP) framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity. We analyze our framework using three real-world domains including education, local restaurants, and hospitality. We assert that our framework simplifies prompt engineering for general users to produce effective results while minimizing the exposure of sensitive user data to search engines. We also present a one-of-a-kind Large Language Model (LLM) peer assessment of the ChatReview framework. Specifically, we employ Google’s Bard to objectively and qualitatively analyze the various ChatReview outputs. Our Bard-based analyses yield over 90% satisfaction, establishing ChatReview as a viable survey analysis tool.