Homoseksual Menurut Fiqh Al-Hadith: Kajian Kes Liwāṭ Dalam Kalangan Pelajar Madrasah Terpilih di Selangor, Malaysia

Due to its tightly coupled structure, intractable and unpredictable workflow and complex interaction of its elements, the construction industry’s work environment (i.e., vertical, horizontal or underground construction projects) is acknowledged as a complex sociotechnical system (STS). STS theory studies the interrelationship of components within a complex system rather than inquiring about its integral components in isolation. To expand knowledge and application of the STS perspective in construction safety management, this study generates a new (STS thinking based) conceptual model through systematic review and analysis of extant literature on construction safety management. An overarching epistemological design embraced interpretivist, critical realism and inductive reasoning to analyse bibliometric data of 2,227 articles retrieved from the Scopus database. Thematic analysis of bibliometric data that was conducted through manual screening of ‘titles’, ‘abstracts’ and ‘keywords’ revealed eleven distinctive thematical clusters of research activity within the prevailing body of knowledge, viz.: ‘contractor relationship’, ‘lean management’, ‘policy and regulations’, ‘prevention through design’, ‘safety culture’, ‘safety management strategies’, ‘safety of machinery’, ‘safety of sites’, ‘safety of workers’, ‘sustainability and waste management’ and ‘technology’. From these aforementioned clusters, ‘safety of workers’, ‘safety of machinery’ and ‘safety of sites’ thematic clusters were selected for further content analysis using the NVivo qualitative data analysis tool. The results from both thematic and content analysis form the basis of a novel theoretical conceptual model (entitled ‘safety-in-cohesion’) that provides palpable insights and guidance for academics and industry practitioners to reflect upon the design, development and analysis of safety management systems based on a holistic perspective of both social and technical aspects. Specifically, the ‘safety-in-cohesion’ model: combines pertinent elements of Safety-I and Safety-II principles that apply to construction safety management. It also promotes continuous learning (through relational loop between design and construction stages) based on integration of different project stakeholders, different data types (i.e., lagging and leading indicators) and different work modes (i.e., reactive and proactive). Emergent findings and concomitant polemic discussion present an unconventional perspective on construction safety within a complex (and often bespoke) STS system on site and generate new avenues of research direction for scholars.

The research aimed to evaluate the impact of a narrative approach called the Tree of Life which was used to support refugee CYP in schools who had been identified as vulnerable. This study used a mixed method design. Quantitative data were collected from all participating children as part of the service evaluation using a survey method with questionnaires. The qualitative analysis was done using thematic analysis with Interpretive phenomenological orientation. The quantitative analyses showed overall positive and significant differences before and after the Tree of Life group, and in particular, in terms of increased self-confidence, clarity about future goals, enjoying school, motivation and improved parental understanding. Surprisingly, no significant change was found in the participants feeling of being proud of their cultural Heritage. Seven categories (clusters of interrelated themes) were identified: Tree of Life Condition/Supportive Environment (characterised by non-judgemental, respect and trust); Cultural heritage (a complex category which embodied cultural challenges, dislocation, identity); Future goal (aspirations, hopes and dreams); Understanding of self and others; Emotional experience (including enjoyable experience); Learning; and Positive Change. In conclusion, the qualitative results gave substantive evidence of how Tree of Life groups can have a positive impact on the lives of children and young people from refugee communities. This includes increased confidence, positive behavioural change and improvement in social relationships (with friends, family and at school).

The objective of this paper is to describe two methods of qualitative analysis - thematic analysis and content analysis - and to examine their use in a mental health context. A description of the processes of thematic analysis and content analysis is provided. These processes are then illustrated by conducting two analyses of the same qualitative data. Transcripts of qualitative interviews are analysed using each method to illustrate these processes. The illustration of the processes highlights the different outcomes from the same set of data. Thematic and content analyses are qualitative methods that serve different research purposes. Thematic analysis provides an interpretation of participants' meanings, while content analysis is a direct representation of participants' responses. These methods provide two ways of understanding meanings and experiences and provide important knowledge in a mental health context.

There is little research on what is meant by the concept of "feeling attracted" and even less about what same-sex attraction looks and feels like for individuals. Without insight into the phenomenon of same-sex attraction, researchers risk misunderstanding the role of sexual attraction in sexual identity development and risk mis-categorizing individuals in research designs that compare LGBTQ and heterosexual samples. The current study draws from semi-structured interviews (n=30) with young lesbian-, bisexual-, and queer-identified women (ages 18-24) about their initial memories of same-sex attraction. Two questions were pursued using qualitative analytic strategies. We examined the age that participants remembered first experiencing same-sex attraction using content analysis. Two age groups emerged as distinct: those with experiences of same-sex attraction in childhood and those with initial attractions in later adolescence. We also examined key elements in participants' descriptions of early same-sex attraction using thematic analysis. The role of embodied feelings, relationships with other young women, and social environments including media images emerged as central to initial experiences of attraction. Findings highlight how early experiences of same-sex attraction produced different types of interpretations within individuals and, in turn, these interpretations informed how participants did or did not take up LGBTQ identity labels. These findings may help guide the development of more refined measurement tools for researchers hoping to sample sexual minorities and can contribute to developing more effective supports for individuals who experience same-sex attraction but may not adopt LGBTQ identity labels and, as a result, are routinely missed in outreach efforts.

Polish immigrants are currently the largest foreign-born population in the UK. Nevertheless, their needs appear somewhat invisible. Research indicates that, despite experiencing high levels of psychological distress, the utilisation of psychological services amongst Polish individuals is low. This thesis explores factors affecting the psychological wellbeing of Polish immigrants, their access to services and their experience of accessing them. It also explores the understanding of this client group amongst psychologists, their training needs and ways to improve services. The study adopts a mixed methods design. An online survey was conducted amongst psychologists and the gathered data was analysed using content analysis. Semi-structured interviews were conducted with members of the Polish community who have accessed psychological help. Data was analysed using thematic analysis. Psychologists responding to the survey acknowledged the impact of migrationrelated, socio-economic and cultural factors on the wellbeing of Polish clients. In considering access to services, the majority of responses located the barriers within the Polish community (e.g. help-seeking attitudes). Analysis of the interviews with Polish people who had accessed services identified three overarching themes encompassing the role of cultural factors, conceptualisations of distress, negotiation of migrant identity and the relationship with the NHS. The theme ‘Occupying dichotomous positions’ describes immigrants’ position in relation to time (present/past), place (Poland/UK) and identity (Victim/oppressor, different/the same). The second theme ‘Help through Polish cultural lenses’ contains sub-themes conceptualising therapy as culturally unfamiliar, seeking help as connected to aspects of pride and shame, and perception of services as unwilling to help. The final theme, ‘Understanding beyond language’, conveys the importance of understanding the historical, political and cultural context as well as appropriately addressing language barriers. Based on the findings, implications for clinical practice and future research are considered. The findings suggest that there is a need to improve the understanding of the Polish community within mainstream psychology services, and efforts should be made to improve the relationship between Polish communities and the NHS. The need to develop Community Based Participatory Action Research projects with the Polish community was highlighted as one of the future research implications.

Research has a long tradition of quantitative research which still dominates many university courses on research methods. Qualitative research is a younger phenomenon that was established in research after the second world war. An emerging research field that needed new analysis methods tailored for qualitative data. Two of the most frequently used approaches in qualitative data analysis are content analysis and thematic analysis. In several aspects content analysis and thematic analysis both share a common approach to analytically examine qualitative data, and the fact that they have been used interchangeably has made it difficult for the more unexperienced researchers to distinguish and choose between them. The aim of this study is to examine doctoral students’ perceptions of qualitative analysis with content analysis and thematic analysis. The study had a qualitative approach with data collected from two webinars on qualitative data analysis, where a total of 76 doctoral students participated. Data consists of participant reflections in a Padlet on content analysis and thematic analysis at the two webinars. Webinar participants have given their consent to use their reflections in the Padlet for research. Content analysis with an abductive coding approach was used to analyse the collected data and formulate categories that answer the study’s aim and research question. Results show both perceived similarities and differences between content analysis and thematic analysis. Both are perceived to have a similar process in the coding of data, although content analysis has a wider selection of coding approaches and thematic analysis support deeper immersion. Content analysis is also perceived as more practical and straightforward, while thematic analysis is perceived as more intuitive and faster to learn. Both content analysis and thematic analysis are perceived to have individual opportunities and challenges that make them appropriate for different types of research. Findings presented in this study can be used by researchers at any level to explore similarities and differences between content analysis and thematic analysis, and where to apply them in research.

This study aims to determine: (1) online game addiction of junior high school students in Kediri City, (2) junior high school students' learning achievement, (3) and the relationship between online game addiction and learning achievement. Mixed Methods Design is used as a combination or combination of quantitative and qualitative research approaches including qualitative and quantitative data in a single study. The strategy used, namely the Congruent Embedded Strategy, is an interesting research strategy, namely in one stage of data collection, researchers are able to collect two types of data together. The results of the quantitative research show that students who are addicted to online games have 3 students (16.03%) including very high, 4 students (14.36%) including high, 13 students (41.86%) including moderate, 4 students (11.9%) is low, and 5 students (12.66%) is very low. In addition, there are 12 students who have low learning achievement, namely getting the "less" and "very poor" categories so that they get an unsatisfactory ranking. While there are 6 students who get the "enough" category and 12 other students get the "very good" and "good" categories. From these results, there is no significant relationship between online game addiction and learning achievement because the value of the hypothesis test is 0.291. On the qualitative side, from 30 students it was found that (1) His early playing online games, (2) The average time spent playing 2-4 hours, (3) His parents did not allow him to play online games, and (4) Achievement low learning.

The present study analyzes teachers' beliefs by looking at the value teachers give to the languages embodying multilingualism in the south of the Basque Country (Basque, Spanish, English and French). The research has a mixed methods design. On the quantitative side, the sample consists of 1093 in-service, pre-service and university teachers who completed an online questionnaire about multilingualism. On the qualitative side, 20 in-service teachers participated in several focus group discussions. The study revealed that the participants hold strong positive beliefs about a Basque-focused trilingualism. The data confirm protective beliefs toward the minority language, Basque, but also reaffirm the integrative and supportive beliefs of teachers toward multilingualism and the inclusion of other languages, especially English. Although these teachers connect multilingualism with natural, positive and enriching characteristics, our data show that they do not seem to value French. A major implication of the findings is that teachers' beliefs align with the current school of thought supporting up-to-date multilingual approaches in a context where a minority language under revitalization process coexists with a national-state language and a lingua franca.

BackgroundTwitter’s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content.ObjectiveThe aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content.MethodsWe conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18.ResultsResults suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA.ConclusionsWe propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

The article presents a modern approach to analysing public opinion based on Ukrainian-language content from Telegram channels. This study presents a hybrid clustering approach that combines DBSCAN and K-means algorithms to analyse vectorised Ukrainian-language social media posts in order to detect public opinion trends. The methodology relies on a multilingual neural network–based text vectorisation model, which enables effective representation of the semantic content of posts. Experiments conducted on a corpus of 90 Ukrainian-language messages (collected between March and May 2025) allowed for the identification of six principal thematic clusters reflecting key areas of public discourse. Despite the small volume of the corpus (90 messages), the sample is structured and balanced by topic (news, vacancies, gaming), which allows you to test the effectiveness of the proposed methodology in conditions of limited data. This approach is appropriate in the case of the analysis of short texts in low-resource languages, where large-scale corpora are not available. A special advantage of this approach is the use of semantic vector representation and the construction of graphs of lexical co-occurrence networks (term co-occurrence networks), which demonstrate a stable topological structure even with small amounts of data. It allows you to identify latent topic patterns and coherent clusters that have the potential to scale to broader corpora. The authors acknowledge the limitations associated with sample size, but emphasise the role of this study as a pilot stage for the development of a universal, linguistically adaptive method for analysing public discourse. In the future, it is planned to expand the body to increase the representativeness and accuracy of the conclusions. The paper proposes a hybrid method of automatic thematic cluster analysis of short texts in social media, in particular Telegram. Vectorisation of Ukrainian-language messages is implemented using the transformer model multilingual-e5-large-instruct. A combination of HDBSCAN and K-means algorithms was used to detect clusters. More than 36,000 messages from three Telegram channels (news, games, vacancies) were analysed, and six main thematic clusters were identified. To identify thematic trends, a hybrid clustering approach was used, in which the HDBSCAN algorithm was used at the first stage to identify dense clusters and identify "noise" points, after which K-means were used to reclassify residual ("noise") embeddings to the nearest cluster centres. Such a two-tier strategy allows you to combine the advantages of flexible allocation of free-form clusters from HDBSCAN and stable classification of less pronounced groups through K-means. It is especially effective when working with fragmented short texts of social networks. To validate the quality of clustering, both visualisation tools (PCA, t-SNE, word clouds) and quantitative metrics were used: Silhouette Score (0.41) and Davis-Boldin index (0.78), which indicate moderate coherence and resolution of clusters. Separately, the high level of "noise" in HDBSCAN (34.2%) was analysed, which may be due to the specifics of short texts, model parameters, or stylistic fragmentation of Telegram messages. The results obtained show the effectiveness of combining modern vectorisation models with flexible clustering methods to identify structured topics in fragmented Ukrainian-language content of social networks. The proposed approach has the potential to further expand to other sources, types of discourse, and tasks of digital sociology. As a result of processing 90 messages received from three different channels (news, gaming content, and vacancies), six main thematic clusters were identified. The largest share is occupied by clusters related to employment (28.2%) and security-patriotic topics (24.7%). The average level of "noise" after the initial HDBSCAN clustering was 34.2%. Additional analysis revealed that post lengths varied significantly, ranging from short announcements (average of 10 words) to analytical texts (over 140 words). Visualisations (timelines, PCA, t-SNE, word clouds, term co-occurrence graphs) confirm the thematic coherence of clusters and reveal changes in thematic priorities over time. The proposed system is an effective tool for detecting information trends in the environment of short, fragmented texts and can be used to monitor public sentiment in low-resource languages.

To explore factors influencing the implementation of a nursing care delivery model in a hospital setting. A qualitative evidence synthesis with a thematic synthesis was conducted. The search string consisted of four 'cluster topics': (1) nursing, (2) care delivery models, (3) hospital setting, (4) qualitative and mixed methods designs. Four electronic databases were searched from January 2000 until July 2024: MEDLINE (PubMed interface), Embase (embase.com interface), CINAHL (EBSCOhost interface) and Web of Science. A thematic synthesis was conducted consisting of the following steps; the 'line-by-line' coding of the text, the development and allocation of 'descriptive themes' and the generation of 'analytical themes'. In total, 3976 references were screened, of which 25 were included in the qualitative evidence synthesis. Eight analytical themes were generated that influence the implementation of a nursing care delivery model in a hospital setting: shared understanding of the care delivery model, ownership of the change, scope of practice and role clarity, collaboration, communication, responsibility, a double-loop process and aggregated recommendations. The themes were categorised on four different levels: vision, process, interactional factors and contextual factors. The eight themes identified in this qualitative evidence synthesis showed that during the implementation of a nursing care delivery model, a clear implementation strategy is often missing. It is advised that future implementation processes have a clear guide and goal. The analytical themes can guide the future implementation of a new nursing care delivery model in a hospital setting. This review can support nurses, researchers, hospital management and policymakers when implementing organisational alternatives to reorganise nursing care in a hospital setting. The qualitative evidence synthesis was reported according to the enhancing transparency in reporting the synthesis of qualitative research (ENTREQ) statement. No patient or public contribution.

This study proceeds with a critical view of Grounded Theory (GT) and Thematic Analysis (TA) as possible bases for qualitative research, intending to make their philosophical underpinnings, methodological procedures, practical application, and ongoing relevance clearer. The purpose is to help researchers decide between methods through consideration of the epistemological assumptions, analytic procedures, and usage trends in GT and TA. It looks at the strengths, weaknesses, and practical considerations of each method, while maintaining a view of what gaps remain and giving suggestions for improved practice. Methodologically, the paper critically synthesizes the literature to trace GTs' and TA's development, use, positioning in epistemology, and their considerations regarding the research method. It examines peer-reviewed documents, including the original texts and more recent developments in constructivist GT and reflexive TA. The main differences are elaborated in terms of data collection, coding, theoretical expectations, and applicability to digital tools and interdisciplinary research. Findings reveal that GT provides an orderly and rigorous framework for theory generation through the processes of iterative coding, theoretical sampling, and constant comparison. However, the GT approach continues to remain mired in terms of epistemological uncertainty, difficulties in attaining complete theoretical saturation, which demands high methodological skill on the part of the practitioner. TA, in contrast, offers rather straightforward and flexible techniques to pinpoint patterns that could be contrasted across different data sets. TA's reflexive and codebook types are suitable for different analytic needs; however, TA suffers from risks of superficial interpretations, inconsistent theme development, and lack of methodological transparency. The review contributes to qualitative methodology by embracing the comparative viewpoint to shed light upon the philosophical and procedural discrepancies between GT and TA, thus guiding researchers toward an informed selection of methods. It further highlights the need for improvement in epistemological clarity, digital literacy, and reflexive practice to build more rigorous analyses. The paper proceeds to suggest ways to combine GT and TA in mixed-methods designs as a step toward theoretically and methodologically coherent innovation. In sum, the review acts as a call to responsible methodological pluralism to move qualitative research forward in current times.

Adult offspring of lesbian parents: How do they relate to their sperm donors?

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.