Qualitative Textual Data Research Articles

Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment”

This study employs computational hermeneutics to explore emotional representations in Fyodor Dostoevsky’s Crime and Punishment. Using advanced Natural Language Processing (NLP) techniques, including sentiment analysis powered by the BERT model and other tools, we examine the emotional and existential aspects embedded in the narrative. Our approach combines computational tools with traditional hermeneutic methods to deconstruct the emotional journey of the protagonist, Rodion Raskolnikov. We quantitatively analyze his emotional evolution before and after the pivotal crime, mapping his psychological transition from detachment to engagement. The study focuses on four key emotional dimensions: Attitude, Introspection, Sensitivity, and Temper. By transforming qualitative textual data into a quantitative framework, we provide a novel illustration of Raskolnikov’s psychological progression from a state of depersonalization to full emotional contact. This computational analysis is contextualized within a broader philosophical interpretation, drawing on concepts such as Stoic apatheia and Heideggerian Sorge. Our research serves a dual purpose: it enhances our understanding of Dostoevsky’s work while demonstrating the potential of computational methods in literary analysis. By bridging digital humanities and traditional literary criticism, this study contributes to the growing field of digital hermeneutics and opens new avenues for interdisciplinary research in literature, philosophy, and computational linguistics.

Climate change considerations in environmental impacts assessment (EIA) practices: learning from climate sensitive projects in Rufiji Basin Tanzania

Abstract The assessment of climate change mitigation and adaptation in environmental impact assessment practices has been mostly inconsistently addressed. Studies that use data validation from experts and relevant authorities to address the socio-political context that influence the process have been inadequate. This study aimed to bridge the gap by examining the extent to which climate change mitigation and adaptation in environmental impact assessment practices in Tanzania have been considered and the broader social-political context that influence the process. A mixed research design employing sequential methods was adopted. Both primary and secondary data was used. Primary data was obtained from 22 environmental experts through a close-ended questionnaire and interview with 6 key informants, while secondary data was obtained from 19 environmental impact assessment reports issued between January 2020 and January 2022. Purposive sampling was used to select key informants and simple random sampling was used to select environmental experts and environmental impact assessment reports. Content analysis was employed to assess qualitative text data to generate descriptive information. SPSS enabled the processing and analysis of quantitative data through frequencies and percentages. The findings revealed that 23.3% and 10.5% of reviewed reports considered climate change mitigation, and adaptation respectively, reflecting that the degree of consideration was low and was mainly limited to mitigation. A close link between the environmental impact assessment legal frameworks and the environmental impact assessment practices was observed and it was recommended for a review of basic frameworks to mandate and include details on climate change mitigation and adaptation assessment in environmental impacts assessment practices. The study recommends building capacity of experts and authority, preparing clear climate change assessment guidelines, and availing reliable climate data at the local level to facilitate modelling.

The Impact of a Company’s Management Strategy on Its Profitability, Stability, and Growth: A Focus on the Information Security Industry

This study concentrated on a business report that typically reveals a company’s non-financial information, aiming to uncover its strategic direction. Using text-mining techniques, the research extracted and analyzed the report’s overview sections, identifying key strategic themes categorized into the financial, customer, learning and growth, and internal process perspectives. The empirical analysis applied a two-stage model to assess how shifts in company strategies affect profitability, stability, and growth. This research provided insights into the management strategies and financial metrics within the information security sector, examining how strategic priorities shape financial health. The findings were as follows. Firstly, companies emphasizing financial strategies in their reports tended to exhibit higher profitability. Secondly, those focusing on customer-oriented strategies also reported greater profitability. Thirdly, companies prioritizing internal processes demonstrated increased organizational stability. Fourthly, an emphasis on learning and growth strategies was associated with lower stability but higher growth potential. This paper contributes to the field by offering a method to quantitatively analyze qualitative textual data, providing a more precise approach to understanding management strategies through direct content analysis of business reports. It also highlights the specific financial and strategic characteristics of information security firms, a relatively under-researched area, thereby offering valuable guidance for these companies in terms of strategic planning.

Longitudinal Sentiment Analysis with Conversation Textual Data

AbstractThe inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models. This paper presents a two-stage process for analyzing longitudinal textual data, offering a solution to this inherent challenge. The proposed model comprises (1) initial data preprocessing and sentiment extraction, followed by (2) applying a growth curve model to analyze the extracted sentiments directly. The paper also explores four distinct approaches for extracting sentiment scores in the dialogue, providing versatility to the proposed framework. The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset. This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data, providing a robust methodology for extracting and analyzing sentiments longitudinally.

EVALUATION OF NIGERIAN CASHLESS PAYMENT POLICY AND ITS IMPACT ON ECONOMIC GROWTH: A SYNTHETIC REVIEW

This study aimed to provide a comprehensive analysis of the existing literature on the impact of cashless payment policy on economic growth in Nigeria. The paper is designed to provide a first broad overview for academic researchers and provide a practical financial policy for determining the impact of cashless payment policy on economy growth. This study conducted a critical literature review (survey) on the relation between cashless payment policy and economic growth. This research was explorative, descriptive and was based on documentary review. The study applied qualitative text data analysis approach and examined documents and systematically interprets the information so as to allow the researcher to draw inference about the issues of interest. Thus, the identified articles were screened using the content-centric analysis technique so as to align with the objective of the study by searching for suitable keywords in line with the theme of the study. Hence, the study was carried out systematically through an article search engine using the keywords "cashless policy" and "economic growth,". Having evaluated the outcome of several study from the review, the study finds that virtually all the studies agreed that cashless payment policy has significant impact on economy growth of a country. However, due to different economic structure and levels of technology adoption as well as other related factors as explained in this study, the positive impact of cashless payments on economy growth in Nigeria may not be generalizable to European countries and other member countries such as G7, G20, OECD and CPMI as reviewed in the study.

Barriers in Cleft Service Access in Sub-Saharan Africa: A Thematic Analysis of Practical Needs of Rural Families.

To explore the experiences and perceptions of barriers of parents and family members of patients with cleft lip and palate in accessing cleft services in remote northwest Nigeria. Face-to-face semi-structured audio recorded interviews were used to obtained qualitative textual data. Thematic analysis using interpretative descriptive techniques was employed to understand the participants' lived experiences with barriers and accessibility to cleft services. Participants were from Sokoto, Kebbi and Zamfara states in remote northwest, Nigeria. Consisted of 22 caregivers (17 parents and 5 extended family members) were purposively sampled between 2017 and 2020. Barriers experienced while accessing cleft services were identified during thematic analysis. Over three quarter of the respondents had patients with both cleft lip and palate and without any previous family history (n = 20). About two-thirds of the participants (n = 15) were females. Most of the interviews were conducted before the surgeries (n = 15). lack of information, financial difficulty, misrepresentation from health workers, multiple transportation and previous disappointment. Areas of poor awareness, misinformation from primary health care workers, financial hurdles, multiple transportation logistics and others were identified. Aggressive broadcasting of information through radio, timely treatment and collaboration with influential religious leaders were emphasized. Support, grants and subsidies from government and voluntary agencies are encouraged to mitigate the huge out of pocket cost of cleft care in the region.

Factors Affecting Timely Remittance of Tax Returns in Lusaka, Zambia: A Case of Chachacha and Freedom Way Block

The objective of this study was to explore the factors behind the timely remittance of tax returns by businesses in Lusaka. The purpose was to conduct a mixed-methods case study of the factors affecting timely remittance of tax returns amongst small businesses in Lusaka’s Chachacha-Freedom Way Tax Block. Data was collected from a purposive sample of 159 small business owners and managers in the Chachacha and Freedom Way Blocks in Lusaka, Zambia. A self-administered questionnaire was the tool used for data collection. Categorical data was analyzed by simple frequencies using SPSS Version 26. Qualitative textual data was analyzed manually using hierarchical coding frames. Quantitative Likert scale data was used to estimate PROBIT empirical models to investigate how factors such as perceptions of administrative simplicity, economic efficiency, equity, flexibility, certainty, and political responsibility around the fiscus affect the probability of timely remittance of returns. Results show that experiential issues were some of the reasons affecting taxpayers that could be analyzed in terms of the conduct of their businesses, interaction with tax authorities, and information flows between them. Factors contributing to delayed remittance of tax returns included non-adoption of e-filing methods and perceptions of political responsibility or being otherwise behind the tax system and fiscus. The study therefore finds some evidence for the political legitimacy theory of tax compliance. Both the quantitative and qualitative analyses conducted generally supported the idea that perceptions of economic efficiency, administrative simplicity, equity, flexibility, certainty, and political responsibility generally contribute to the timely remittance of tax returns. Recommendations of the study include promotion and easing of user friendliness of e-filing methods, as well as raising the ethical profile of the authority and strengthening taxpayer belief in the value of paying taxes through strategic partnerships with tax-funded government operations to improve political legitimacy.

Signs of nature in Bediuzzaman Said Nursi: A significant contribution to Islamic semiotics

In the life circle, humans always produce, receive, and search for the meaning of signs. The meanings have ultimate value concerning Islamic worldview which then determines human salvation. Therefore, these issues are very important to research. The researchers worked in a deductive-inductive approach: a quasi-qualitative method characterized by the use of theories to be reconstructed after critical dialogues aimed at revealing the meanings of the natural signs based on an Islamic worldview and proposing a model of the Islamic theory of sign. The subject of this research is the books of Risale-i Nur as the representation of the Islamic worldview. Further, the techniques of the data collection are (1) preparing the domain (2) constituting the taxonomy of the data; (3) note-taking in the text of the Risale-i Nur. Furthermore, the latent content analysis was employed. The researchers categorized qualitative textual data into clusters of similar entities, or conceptual categories, to identify consistent patterns and relationships between themes. Finally, the researchers concluded that the meanings of the natural signs in the Risale-i Nur are the manifestation of the perfection of God’s Names. Moreover, the significant contribution of the Risale-i Nur to Islamic semiotics is revealed and proposed.

Factors Affecting Private Sector Partnerships with Non-Governmental Organizations in Zambia: A Case Study of Musokotwane Compassion Mission Zambia

The objective of the study was to determine how NGO-private sector partnerships can be forged to raise resources and funding for their activities that supplement government efforts. Data was collected from a purposive sample of 83 staff. The study utilized a questionnaire with both closed-ended and open-ended questions for data collection from the respondents. The study adopted a mixed-methods approach. Correlation and regression analyses were used to establish the relationship among the variables of the study, and qualitative textual data was analyzed. The results of the study show that governance, transparency, political intervention, and decision-making processes are key determinants of partnership performance between the private sector and non-governmental organizations in Zambia. The study further established that lack of transparency in donor fund administration and management, corruption, poor governance, and decision-making hugely contribute to the poor performance and sustenance of these partnerships. The study recommends that NGOs should possess the highest professional and ethical standards and exercise responsible resource management to obtain funding from donors. It is also recommended that NGOs be as accountable and transparent as possible to establish confidence-driven partnerships with the private sector, as these are key issues affecting these partnerships.

The role of law in end-of-life decision-making in emergency departments and intensive care units: a retrospective review of current practice in a Queensland health service.

Objective There is limited evidence about how legal frameworks that underpin end-of-life decisions are applied in practice. This study aimed to identify how end-of-life decisions are made and documented in emergency departments and intensive care units. The secondary aim was to explore the extent to which the legal processes featured in these decisions. Methods A retrospective chart audit of 85 adult patients who died in the emergency departments and intensive care units of a Queensland health service was undertaken. Quantitative data were analysed and reported using descriptive statistics. Qualitative textual data were analysed using inductive content analysis. Results Nearly all admissions were unplanned (97.6%), and most patients (74.1%) were admitted from home. Only one patient had an advance health directive, although all had an eligible substitute decision-maker. The qualitative analysis revealed two main concepts - 'healthcare professionals choreograph the end of life' and 'patients and families are carried on an unplanned journey'. Conclusions There was limited documentation related to the application of the legal framework in these decisions. Healthcare professionals relied on their clinical judgment about what was in the best interest of the patient. It was common for there to be a substantial effort to achieve consensus in decision-making which coincidently complied with the law.

Local Political Dynamics in Village Head Elections in Damai Village, Sidenreng Rappang Regency, Indonesia

This study aims to determine the dynamics of local politics in the 2021 Election of Peaceful Village Heads in Sidenreng Rappang Indonesia. This research uses a qualitative descriptive method. Data collection techniques used were observation, interviews, field notes, and documentation. Resource persons were selected by purposive sampling. Data analysis namely data reduction, data presentation, and conclusions. Data analysis, namely data reduction, data presentation and conclusions displayed through Nvivo 12 Plus Software visualization. NVivo 12 Plus was chosen because it is capable of producing qualitative text data in the form of attractive data visualizations. The results of this study indicate that the local political dynamics that occurred in the Peace Village head election led to polarization in the village community structure. The election of the village head still leaves a mark in which there has been a rift in relations between the people who still have close family ties. The dynamics occur in three phases, namely pre-election, implementation of the election, and post-election. The behavior of the voting community in Damai Village is still dominated by the behavior of traditional voters. The occurrence of community polarization in Damai Village is the result of the political behavior of the people who are still traditional.

The diversity of providers’ and consumers’ views of virtual versus inpatient care provision: a qualitative study

BackgroundA broad-based international shift to virtual care models over recent years has accelerated following COVID-19. Although there are increasing numbers of studies and reviews, less is known about clinicians’ and consumers’ perspectives concerning virtual modes in contrast to inpatient modes of delivery.MethodsWe conducted a mixed-methods study in late 2021 examining consumers’ and providers’ expectations of and perspectives on virtual care in the context of a new facility planned for the north-western suburbs of Sydney, Australia. Data were collected via a series of workshops, and a demographic survey. Recorded qualitative text data were analysed thematically, and surveys were analysed using SPSS v22.ResultsAcross 12 workshops, 33 consumers and 49 providers from varied backgrounds, ethnicities, language groups, age ranges and professions participated. Four advantages, strengths or benefits of virtual care reported were: patient factors and wellbeing, accessibility, better care and health outcomes, and additional health system benefits, while four disadvantages, weaknesses or risks of virtual care were: patient factors and wellbeing, accessibility, resources and infrastructure, and quality and safety of care.ConclusionsVirtual care was widely supported but the model is not suitable for all patients. Health and digital literacy and appropriate patient selection were key success criteria, as was patient choice. Key concerns included technology failures or limitations and that virtual models may be no more efficient than inpatient care models. Considering consumer and provider views and expectations prior to introducing virtual models of care may facilitate greater acceptance and uptake.

MUTU PENDIDIKAN ISLAM DALAM PERSPEKTIF AL-QURAN DAN TAFSIR

The goal of this study is to find out how the Koran and its interpretation describe the quality of Islamic education. The literature review incorporates the conducted search. This qualitative research is part of the kind of library research that uses analytical and descriptive methods. The qualitative descriptive method was analyzed in this study using a variety of approaches. The first step in this study is to examine everything in the Koran that ensures Islamic education quality. Second, examine all of the Hadith, Koran, and other verses that are comparable to education. Thirdly, investigate the verses of the Koran, the Hadith that describes the Prophet Muhammad SAW's daily activities, and the components of the quality of education. The fourth step is concentrating on science, which can be as an essential hypothesis or idea with respect to the nature of Islamic instruction from the stanzas of the Qur'an and hadith, or regular occasions. Fifth, make inferences based on the analysis's findings. The qualitative textual data used in this study combines primary, secondary, and complementary data. the kind of education that is well-liked by the community as well as schools, as well as the kind of education that can assist students in developing relative reasoning. Schools and quality are working to foster an environment where learning the Koran and its translation can be used as an educational experience, particularly in granting the best wishes.

Analisis Sentiment Review Pengguna BCA Mobile Menggunakan Teks Mining

The purpose of this research is to analyze the sentiment that exists in mobile banking and what aspects are the basis for assessing user sentiment. The method used is text mining using the Naïve Bayes algorithm in Python. The type of data used is qualitative text data. Data is collected from user reviews of mobile banking applications on the Google Play Store. The results of this study found BCA Mobile has a positive sentiment with a Positive TN value of 44% with an accuracy value of 82%. As for the confusion matrix results of each sentiment class, the Precision value in the positive sentiment class is 87%, in the negative class is 79%, the Recall value of the positive class is 72%, in the negative class is 91%, and the F1-Score value of the positive and negative classes is 79% and 84%, respectively. This assessment is reviewed from several aspects of the reviews given by users such as the verification process, ease of use, security, and features presented.

Statistical Depth for Text Data: An Application to the Classification of Healthcare Data

This manuscript introduces a new concept of statistical depth function: the compositional D-depth. It is the first data depth developed exclusively for text data, in particular, for those data vectorized according to a frequency-based criterion, such as the tf-idf (term frequency–inverse document frequency) statistic, which results in most vector entries taking a value of zero. The proposed data depth consists of considering the inverse discrete Fourier transform of the vectorized text fragments and then applying a statistical depth for functional data, D. This depth is intended to address the problem of sparsity of numerical features resulting from the transformation of qualitative text data into quantitative data, which is a common procedure in most natural language processing frameworks. Indeed, this sparsity hinders the use of traditional statistical depths and machine learning techniques for classification purposes. In order to demonstrate the potential value of this new proposal, it is applied to a real-world case study which involves mapping Consolidated Framework for Implementation and Research (CFIR) constructs to qualitative healthcare data. It is shown that the DDG-classifier yields competitive results and outperforms all studied traditional machine learning techniques (logistic regression with LASSO regularization, artificial neural networks, decision trees, and support vector machines) when used in combination with the newly defined compositional D-depth.

Natural Language Processing for Automated Classification of Qualitative Data From Interviews of Patients With Cancer

ObjectivesThis study sought to explore the use of novel natural language processing (NLP) methods for classifying unstructured, qualitative textual data from interviews of patients with cancer to identify patient-reported symptoms and impacts on quality of life. MethodsWe tested the ability of 4 NLP models to accurately classify text from interview transcripts as “symptom,” “quality of life impact,” and “other.” Interview data sets from patients with hepatocellular carcinoma (HCC) (n = 25), biliary tract cancer (BTC) (n = 23), and gastric cancer (n = 24) were used. Models were cross-validated with transcript subsets designated for training, validation, and testing. Multiclass classification performance of the 4 models was evaluated at paragraph and sentence level using the HCC testing data set and analyzed by the one-versus-rest technique quantified by the receiver operating characteristic area under the curve (ROC AUC) score. ResultsNLP models accurately classified multiclass text from patient interviews. The Bidirectional Encoder Representations from Transformers model generally outperformed all other models at paragraph and sentence level. The highest predictive performance of the Bidirectional Encoder Representations from Transformers model was observed using the HCC data set to train and BTC data set to test (mean ROC AUC, 0.940 [SD 0.028]), with similarly high predictive performance using balanced and imbalanced training data sets from BTC and gastric cancer populations. ConclusionsNLP models were accurate in predicting multiclass classification of text from interviews of patients with cancer, with most surpassing 0.9 ROC AUC at paragraph level. NLP may be a useful tool for scaling up processing of patient interviews in clinical studies and, thus, could serve to facilitate patient input into drug development and improving patient care.

Nurses' sources of information to inform clinical practice: An integrative review to guide evidence-based practice.

Evidence-based practice in nursing is challenging and relies on the sources of information used by nurses to inform clinical practice. An integrative review from 2008 revealed that nurses more frequently relied on information from colleagues than information from high-level sources such as systematic reviews and evidence-based clinical practice guidelines. To describe the information sources used by registered nurses to inform their clinical practice. An integrative review was conducted according to the PRISMA guidelines, based on empirical research studies published from January 2007 until June 2021. The included studies were appraised, following which the identified sources of information from quantitative studies were compiled and ranked. Finally, the qualitative text data were summarized into categories. Fifty-two studies from various countries were included. The majority of studies employed a quantitative design and used original instruments. Peers were ranked as the number one source of information to inform nurses' clinical practice. However, computers and reference materials are now ranked among the top four most used information sources. Improvement in computer and information searching skills, as well as the availability of computerized decision support tools, may contribute to nurses' frequent use of digital sources and reference material to inform clinical practice. This review shows that nurses' most frequently reported peer nurses as their source of information in clinical practice. Information sources such as computers and reference materials were ranked higher, and information from patients was ranked lower than in the 2008 review. Developing and standardizing instruments and ensuring high-quality study design is critical for further research on nurses' sources of information for clinical practice.

Data and text mining from online reviews: An automatic literature analysis

AbstractThis paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user‐generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e‐commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences.This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry

“I'm at breaking point”; Exploring pharmacists' resilience, coping and burnout during the COVID-19 pandemic

BackgroundThere is a lack of evidence on how the multimodal dynamic process of resilience has impacted personal adaptation of frontline healthcare professionals, working under extreme pressure during the COVID-19 global pandemic. ObjectivesTo explore resilience, burnout and wellbeing for UK pharmacists in patient-facing roles, including individual and organisational factors that align to the ABC-X theoretical model of the dynamic process of resilience. MethodsA non-experimental pragmatist research design was adopted, with a cross-sectional online survey distributed via social media and professional networks between June and July 2020. Quantitative data aligned to a positivist research paradigm was collected using validated scores, to statistically analyse wellbeing, burnout and resilience. Qualitative textual data, consistent with an interpretivist research paradigm, were analysed following an inductive thematic approach. ResultsA total of 199 surveys from pharmacists working within community, hospital and GP sectors were analysed. Wellbeing scores were strongly correlated to resilience scores. Wellbeing and resilience scores were both inversely correlated with burnout scores. Two-thirds of participants were classified as high-risk within the burnout scales.Key stressors were highlighted by participants, who described how individual resources and perceptions shaped their experience, which overall contributed to their burnout. Organisations that supported pharmacists embraced change and quickly adopted new ways of working, such as teleconsultations, flexible and remote working, redesign of workflow, alongside clear guidance. However, there was also reported frustration at lack of, slow or conflicting guidance from employers. ConclusionsThis study adds to the growing evidence base for how individuals are affected by adverse events in a dynamic environment, alongside the role that employers can play in supporting individual and organisational resilience. It provides an opportunity to learn from pharmacists' responses to the COVID-19 pandemic, and a call to action for healthcare organisations to rebuild and invest resources into sustained support for staff wellbeing.

Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.