Relative Impact Research Articles

Determinants of academic major choice: a comparative study of business programs in Bangladesh

This paper aims to examine the determinants influencing students' choices of academic majors, with a specific focus on business programs at private universities in Bangladesh. It evaluates the relative impact of various factors including subject interest, perceived prestige of the major, faculty reputation, earning potential, and availability of the major. Additionally, this study seeks to analyze how these determinants align with broader socio-economic and institutional contexts. Employing a hybrid analytics method, the study utilizes both quantitative analysis and factor analysis techniques. A dataset of 276 students from three private universities in Bangladesh was surveyed using a structured questionnaire based on a Likert scale. This data was collected over a period that reflects the current trends and perceptions among students regarding their choice of academic majors. The main findings of the study demonstrate that (i) intrinsic interest in the subject significantly influences major choice, (ii) perceptions of economic outcomes associated with the profession, such as earning potential, play a crucial role, and finally, (iii) institutional factors like faculty reputation and the prestige of the major also significantly impact students' decisions. These findings underscore the complexity of factors that students consider when choosing their academic majors, highlighting the interplay between personal interests, economic considerations, and institutional attributes.

The power of embeddedness: how nodal power affects the value appropriation potential of firms in economic networks?

PurposeThis paper explains why some firms manage to capture disproportionate value from their network of relationships, leading to superior performance. The paper examines how a firm's dependencies affect its value appropriation potential (VAP) in economic networks.Design/methodology/approachThe paper follows the axiomatic method and the embeddedness perspective of firms to develop an index called nodal power, which captures the power that accrues to a firm in exchange-based economic networks. Thereafter, using the formal method and simulation, it shows nodal power reflects a firm's VAP in economic networks.FindingsThe study analysis and findings prove that a firm's dyadic level exchange relations and the embedded network structure determine its VAP by affecting the nodal power. A firm with lesser nodal power is likely to appropriate less value from its relations even if it equally contributes to the value creation. This finding explains how the structural and relational characteristics of a firm's network enable disproportionate value appropriation.Practical implicationsNodal power furthers the scope of analyzing firms' economic relationships and changing power equations in dynamic networks. It can help firms build optimal strategic networks and manage the portfolio of relationships by predicting the impact of changing relations on firms' VAP.Originality/valueThe paper's original contribution is to explain, through formal analysis, why and how the structure and nature of relations of firms affect their VAP. The paper also formalizes the power-dependence principle through a dependency-based index called nodal power and uses it to show how interfirm dependencies are key to value appropriation.

The Impact of Human Relations Practice on Organizational Performance: Evidence from Rural Banks in Ghana

Human relation is a definitive management function which help to establish and maintain a mutual line of communication, understanding, acceptance and cooperation between and organization and its human. The purpose of the study was to investigate the impact of human relations practices on organizational performance in selected Rural Banks in the Ashanti Region of Ghana. The study used a quantitative research approach and a non-probability sampling method. The demographic data was evaluated using frequencies and percentages, while the Statistical Package for Social Sciences (SPSS) version 26 was used for the analysis. The study found that effective human relation practices and policies have a significant positive impact on employee satisfaction and ultimately improve the overall organizational performance of banks in the rural areas. It was recommended that organizations should educate their employees on the importance of human relations and how it can improve productivity. Also, management of the selected rural banks should prioritize employ-ee engagement, by creating a positive work environment, providing job security and opportunities for training and development.

Hazard Sensitivities Associated with Ground-Motion Characterization Modeling for the New Zealand National Seismic Hazard Model Revision 2022

ABSTRACT This article summarizes hazard sensitivities associated with the updated ground-motion characterization modeling (GMCM) scheme adopted in the recent revision of New Zealand National Seismic Hazard Model (NZ NSHM 2022). In terms of impact on ground-motion hazard, the current GMCM scheme (GMCM 2022) results in an overall, at times significant, increase in calculated mean hazard with respect to NZ NSHM 2010. With regard to relative impact, the update in GMCM accounts for the dominant change in high-hazard regions, whereas in low-hazard regions update in source characterization model dominate. Within GMCM 2022, the change in shallow crustal ground-motion models (GMMs) dominates the effect on calculated hazard, whereas change in subduction interface GMMs has a compounding effect for east coast of North Island and southwest of South Island. Impact of the two NZ-specific adjustments to some of the published GMMs is also discussed. The back-arc attenuation adjustment accounts for a 20%–30% reduction in calculated hazard for peak ground acceleration in northwest of North Island, whereas aleatory uncertainty adjustment accounts for 10%–20% reduction in high-hazard regions such as along the east coast of North Island and in the lower west of South Island.

A Bibliometric Study of Trends, Indicators, and Characteristics in the Global Scientific Production on Fluorosis.

To perform an analysis of the trends, indicators, and characteristics of the world scientific production on fluorosis, and to evaluate its impact on scientific research in this field. A descriptive, cross-sectional study was conducted with a bibliometric approach of the worldwide scientific production on fluorosis published during the years 2018-2023. All metadata were extracted from the Scopus database. The articles were exported in .csv format to SciVal (Elsevier). Of the collected articles, 69 were original, 17 were reviews, 2 were conference papers, 6 were book chapters, among others. It was found that among the journals with the highest number of articles were Biological Trace Element, Caries Research, Children, Fluoride, and Indian Journal of Forensics. In addition, the highest percentage of authors have published between 1 and 2 articles, with a smaller proportion having 3-4 publications. Among the journals with the highest number of articles are Biological Trace Element, Caries Research, Children, Fluoride, and Indian Journal of Forensics. The Universidade de São Paulo has the most publications, although it is also one of those with the lowest citation-weighted impact in relation to the global average (FWCI: 0.9). The collaboration map shows a wide international cooperation network, with an active participation of Brazil among Latin American countries. Scientific production in fluorosis has a negative trend from 2018 to 2023 and is mainly concentrated in high-impact scientific journals. The study shows a wide network of international cooperation on fluorosis, so the results provide important information to guide future clinically focused research on fluorosis and its impact on public health.

Study on re-entrant hierarchical honeycombs in-plane impact

The introduction of hierarchical structure in cell materials can further improve their energy absorption effect, and negative Poisson's ratio materials have excellent energy absorption capacity and special deformation mode. In this paper, augmented double arrow honeycomb structures is introduced into the re-entrant honeycomb with negative Poisson's ratio as a substructure (RHA) to improve the mechanical properties of the first-order re-entrant honeycomb and enhance the energy absorption effect of the structure. The analytical formula of the collapse stress of honeycomb under quasi-static compression was derived by the two-scale method. The failure stress of RHA under different relative densities and impact velocities is discussed, and the analytical formula of RHA stress in dynamic crushing is derived by combining momentum conservation. Due to the special substructure, the secondary honeycomb discussed in this paper has two plateau periods. In this paper, the second plateau stress of the honeycomb structure is calculated innovatively. The numerical simulation results show that the collapse stress of RHA in the first plateau period is similar to that of the first-order re-entrant honeycomb, and the collapse stress in the second plateau period is increased by 332%. The research in this paper shows that the honeycomb with the second plateau period has a better energy absorption effect, which is an effective strategy for improving the energy absorption effect of the honeycomb. It can be further explored to improve the impact resistance of the honeycomb.

Drought changes the dominant water stress on the grassland and forest production in the northern hemisphere

Low soil moisture (SM) and high vapor pressure deficit (VPD) have a significant impact on the terrestrial ecosystem productivity through different mechanisms. The sources of relative impact of these two water stress factors are difficult to separate due to the strong interaction between the land atmosphere, especially during drought events with an increasing frequency and intensity, leading to significant controversy over the dominant water stress factors in ecosystems. Here, we analyzed the relative effects of SM and VPD on the Gross Primary Productivity (GPP) during non-drought and drought periods and how drought events alter the dominant water stress of the ecosystems using daily observations from 89 flux sites at the Northern Hemisphere. Our results showed that 84 of 89 sites and 81 of 89 sites were significantly affected by water stress during both non-drought and drought periods, with 43 % and 34 % of sites remaining dominant water stress from the high VPD and low SM during non-drought and drought conditions, respectively. In addition, 19 sites have experienced significant dominant water stress shifts when drought events occurred, including forests (47 %), grasslands (37 %), and shrubs (16 %). The normalized GPP by radiation and temperature also emphasizes that high VPD has a stronger control effect on vegetation than low SM and confirmed that drought events could alter the dominant water stress factors in forests and grasslands. Our results might help to clarify the relative impacts of the SM and VPD on the vegetation, as well as the driving forces of drought events on the shifting role of SM and VPD. The driving mechanism for this shifting varies depending on the biomes and plant functional types Furthermore, our findings could provide a more accurate prediction of the vegetation productivity in different ecosystems, and a deeper understanding of the relationship between the vegetation and the climate.

Psychotherapist variables that may lead to treatment failure or termination-A qualitative analysis of patients' perspectives.

A substantial number of patients do not improve from psychotherapy, some even deteriorate, and some terminate treatment prematurely. Identifying therapist variables that may lead to treatment failures from patients' perspectives can inform how psychotherapists can increase effectiveness. Using a semistructured protocol, we interviewed 24 patients who had experienced unsatisfying individual face-to-face psychotherapy within the last 2 years. The study procedures were guided by the consolidated criteria for reporting qualitative research. The manifest content analysis provided 13 subcategories grouped into four categories: (a) psychotherapists' negative traits (inflexible, unengaged, unemphatic, insecure), (b) unprofessionalism (superficial, violating personal boundaries, breaking confidentiality, nontransparent), (c) incompetence (unstructured, poor assessment or understanding, poor knowledge, too passive), and (d) mismatch (therapist-patient mismatch). To reduce the risk of treatment failure, psychotherapists may need a multifaceted set of relational skills, theoretical and technical competence, ethical sensitivity, and engagement. Some of the identified subcategories were complex constructs (e.g., unengaged) that may need time and effort to develop for psychotherapists. Other identified subcategories were obvious inappropriate behaviors in professional psychotherapy (e.g., breaking confidentiality). However, the categories found in this study need further quantitative investigation to assess the validity, frequency, and relative impact on treatment outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Impact of Nigeria-China relations on development in Nigeria, 2010-2020

Nigeria-China relations had a considerable influence on Nigeria's development, and the Peoples Republic of China emerged as one of Nigeria's primary commercial partners and a major source of foreign direct investment (FDI) in recent decades. To this, the relationship between the two nations has influenced Nigeria's growth in various ways, especially in the area of infrastructure development obtained through loans, investments, skills and technology transfer. China has contributed to the development of Nigeria’s infrastructure, thus, fostering economic growth in addition to influencing regional integration. Furthermore, in the energy and power sectors, Chinese firms have been involved in the construction of power plants, transmission lines, and renewable energy projects in Nigeria aimed at addressing power deficits and improving access to electricity, which is essential for industrial development and improving Nigerians' quality of life. China is one of Nigeria's top commercial partners and a substantial source of foreign direct investment (FDI). Chinese enterprises working in Nigeria have provided technology, skills, and knowledge transfer that has benefited Nigerian industries. Chinese investments in industries like telecommunications, manufacturing and agriculture have eased the transfer of technical skills while also increasing efficiency in production methods. This paper aims to illustrate the importance of bilateral trade relations as a means of change and development especially when the nations involved are in need of each other. The paper adopts qualitative approach to the study of international relations. The paper adopts dependency theory to analyze bilateral trade relation between Nigeria and China. The theory believes that trade should only be done to enhance prosperity and also argued that the principle of comparative advantage ensures everyone would profit from the interaction. The paper reveals and concludes that Nigeria-China relations have made a substantial impact on Nigeria's economic development notwithstanding some notable deficiencies that have arisen in areas of unfair labour practices amongst others

Influence of Cell Pore Configuration on Dynamic Mechanical Properties of Honeycomb Structures

With the rapid development of advanced manufacturing technologies such as additive manufacturing, which provides an effective means for processing and preparing a variety of structures with more complex geometrical configurations, the design and study of honeycomb structures have played an important role in promoting the design and study of honeycomb structures. At present, the study of such structures is mainly limited to hexagonal honeycombs, and relatively few studies have been conducted on other cell geometries. In this study, by using the finite element method, we have simulated and investigated the mechanical properties of typical honeycomb structures, in which these structures have different cellular pore configurations and arrangements that are subjected to in-plane low-velocity impact loading. We compared their dynamic load-carrying capacity and deformation patterns with the relative density and impact velocity which is kept constant. Our results show that different cell pore configurations lead to different cell wall stress states during the compression of the honeycomb structure, which affects the macroscopic mechanical properties of the overall structure. Honeycombs with predominantly cell-edge bending have lower stiffness, compressive strength and smoother platform stresses. Honeycombs with deformation modes dominated by cell-wall plastic buckling have the opposite properties. The design of protective structures through further combinatorial optimization of honeycomb structures provides more options to enhance their overall behavior and energy absorption properties.

EE690 Assessing the Relative Budget Impact (BI) of PARP Inhibitor (PARPi) Combination Therapies for Patients with Metastatic Castration Resistant Prostate Cancer (mCRPC) with and Without BRCA1/2 Mutations

EE690 Assessing the Relative Budget Impact (BI) of PARP Inhibitor (PARPi) Combination Therapies for Patients with Metastatic Castration Resistant Prostate Cancer (mCRPC) with and Without BRCA1/2 Mutations

Methanolysis of African pear seed oil catalyzed with acid activated empty palm fruit bunch ash: Optimization and sensitivity analysis

This study investigated the catalytic effect of acid activated empty palm fruit bunch ash (AAEPFBA) on the transesterification reaction of Africa pear seed oil (APSO), optimization of the process and sensitivity analysis. The AAEPFBA was synthesized from waste palm fruit bunch, and modified. The activation was best achieved by adding H3PO4 acid in a ratio of 1:2 (g/ml). The modification of the catalyst increased the alkaline properties and surface area and decreased the particle size and adsorption energy. The biodiesel produced was characterized and compared with standard properties for its application Comparing the three models prediction using response surface methodology (RSM), rsm-genetic algorithm (RSM-GA) and artificial neural network (ANN), the RSM-GA model values of the process variables at temperature of 61.21°C, reaction time of 3.3 h, 10.3:1 methanol/oil molar ratio, 3.16wt% catalyst concentration, and agitation speed of 320.51 rpm gave an experimental optimal yield of biodiesel of 90.1 %. In addition, the RSM, RSM-GA and ANN statistical percentage errors (SPE) are 0.64, 0.06, and 0.29, respectively with RSM-GA having the best prediction of biodiesel yield. The novel model analysis of the process variables connection weight values of MATLAB R2015a shows that the catalyst has the highest percentage (40.7 %) of relative impact on the yield of biodiesel while the methanol/oil ratio has the least percentage contribution less than 5 %. The overall result shows that the catalyst EPFBA activated with phosphoric acid presently has high potential over other catalysts in converting APSO to biodiesel, and the application of RSM-GA tool predict the require values of the variables for maximum yield of biodiesel.

The development of an explainable machine learning system for predicting brain atrophies from electronic health records

AbstractBackgroundThe number of elderly living with dementia is projected to dramatically increase, especially in low‐income and middle‐income countries (LMICs). While obtaining brain scans to detect atrophies and significant radiology findings related to dementia might be costly, the use of electronic health record (EHR) for elderly patients could facilitate identifying patients at a high risk based on routinely collected data. This works, evaluates the use of an interpretable machine learning‐based system that predicts brain atrophies and small vessel disease based on EHR data.MethodWe develop a machine learning predictive system to predict atrophies or small vessel disease from retrospective anonymized EHR data recorded for patients above the age of 50 years from a multispecialty outpatient medical center from Ramallah, Palestine. The patient labelling involved using natural language processing to identify cases in unstructured radiology reports for structural brain Magnetic Resonance Imaging (MRI). For input features, we extracted and preprocessed 30 features for the most commonly collected clinical features such as demographics and laboratory results. The dataset was split into training and test sets, where data preprocessing and augmentation, and hyperparameter tuning were applied before training multiple models including Xgboost, Support Vector Machines, Random Forest, and Logistic Regression. To better understand the relative impact each feature had on the model’s prediction, shapely feature importance analysis was conducted for the top performing model.ResultThe top preforming model was Random Forest, with a performance of Area Under the Precision‐Recall Curve (AUPRC) of 0.989, and 0.842 for Area Under Receiving Operating Curve (AUROC). The feature importance analysis revealed that age, sex, Vitamin D, Vitamin B12 and red blood cell distribution width were the features with highest impact on the model’s predictionConclusionPredicting atrophies from EHRs for elderly visiting outpatient clinics could assist in case‐finding for dementia patients and ultimately help identify patients that would benefit from an MRI. In future works, we aim to further investigate building low‐cost diagnostic tools based on a smaller feature set, which could be valuable for facilities from LMICs.

The Sino-US Trade Conflicts Impact on Semiconductor Stock Index: Chinas Case

On march 22, 2018, U.S. president trump signed a trade memorandum with China at the White House and restricted Chinese companies investment and acquisitions in the united states. The Sino-US trade war officially started. This trade war lasted for more than a year. China has imposed tariffs on each other six times, which has had a great impact on many fields. This paper background is at the point in time when the Sino-US trade war has just ended. The purpose and content of this paper is related impact of the China and US trade war on my countrys semiconductor industry. This article used to analyze the impact of the trade war through the previous data area, and this paper will use the previous data to predict the future semiconductor trend through the ARIMA model (with the Sino-US trade war as the node), and predict that the Sino-US trade war will have little impact on the daily, some impact on the weekly, and a great on the monthly. Based on our results, people can speculate that from a long-terms perspective, and choose to invest in the semiconductor industry.

EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES)

Objective. Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings. Approach. To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters. Main Results. The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI. Significance. Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.

High-risk human papillomavirus prevalence among South African women diagnosed with other STIs and BV.

Human papillomavirus (HPV) infection is a leading cause of cervical cancer. Although this relies on infection and persistence of HPV in epithelial cells, often occurring in the context of other sexually transmitted infections (STIs) and bacterial vaginosis (BV), data on the relationships between these and their relative effects on epithelial barrier integrity in women remain sparse. This study describes the epidemiology of HPV combined with STI and/or BV prevalence and the relative impact on matrix metalloproteinases (MMPs) among South African women. Roche Linear Array was used for HPV genotyping in menstrual cup pellets of 243 HIV-negative women participating in the CAPRISA 083 cohort study. Vulvovaginal swabs were tested for Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis using Xpert® CT/NG assay and lateral flow assay, and Gram staining was performed to diagnose BV using Nugent scoring criteria. Concentrations of 5 MMPs were measured in menstrual cup supernatants by multiplexed ELISA. Fisher's exact tests, Mann-Whitney U tests, and multivariable regression models determined associations between HPV infection, STI and/or BV, and MMP concentrations. HPV was prevalent in 34% of women (83/243; median 23 years, interquartile range (IQR) 21-27 years). Low-risk (lr) (71%, 59/83) and high-risk (hr)-HPV infections (54.2%, 45/83) were common. Hr-HPV was frequently detected in STI and/or BV-positive women compared to women without STIs or BV (p = 0.029). In multivariable analysis, BV was associated with increased odds of hr-HPV detection (OR: 2.64, 95%CI: 1.02-6.87, p = 0.046). Furthermore, Gardasil®9 vaccine-type strains were more frequently detected in women diagnosed with STI and/or BV (55.2%, 32/58 vs 24%, 6/25; p = 0.009). Among STI and/or BV-positive women, HPV detection was significantly associated with increased MMP-10 concentrations (b = 0.55, 95% CI 0.79-1.01; p = 0.022). Most women with hr-HPV had another STI and/or BV, emphasizing an urgent need for STI and BV screening and intensive scale-up of cervical cancer screening and HPV vaccination programmes. Furthermore, the study highlights the need for more extensive research to confirm and understand the relationship between HPV infection and barrier integrity.

TRENDS AND INNOVATIONS IN COSMETIC MARKETING

This study delves into the intricacies of the evolution of cosmetic marketing, shedding light on emerging trends that are redefining how cosmetic products are sold and perceived. It aims to identify the main trajectories of the cosmetics market and thoroughly explore their potential impact on future marketing strategies. The methodologies used in this study include statistical analysis to decipher market dynamics and forecasts, an in-depth literature review to track market narratives, and a comparative approach to understand the relative impact of each trend. Digging deeper into the results, a major trend was the focus on transparency and clarity of product ingredients. Consumers are leaning towards brands that demonstrate unambiguous ingredient lists and transparent sourcing and production practices. The second key trend is the rapid integration of artificial intelligence and big data technologies into cosmetic marketing. Brands are using AI-powered tools to provide personalised product recommendations, predictive marketing, and improve customer experience. In addition, the previously ignored male demographic is becoming a focus, requiring products and marketing campaigns tailored to male sensibilities and needs. The surge in demand for multifunctional cosmetic products, i.e., those that combine multiple benefits, highlights the fourth trend. Consumers are now gravitating towards products that offer multiple benefits by combining skincare and cosmetic benefits. Ultimately, brand loyalty, underpinned by ethical and sustainable practices, is seen as the linchpin of future marketing strategies. The practical implications of this research are multifaceted. For companies operating in the cosmetics market, understanding these trends is not only useful, but also necessary to adapt their strategies to keep them in sync with consumer expectations and ongoing market changes.

Identifying unique exposure-specific transgenerational differentially DNA methylated region epimutations inthe genome using hybrid deep learning prediction models.

Exposure to environmental toxicants can lead to epimutations in the genome and an increase in differential DNA methylated regions (DMRs) that have been linked to increased susceptibility to various diseases. However, the unique effect of particular toxicants on the genome in terms of leading to unique DMRs for the toxicants has been less studied. One hurdle to such studies is the low number of observed DMRs per toxicants. To address this hurdle, a previously validated hybrid deep-learning cross-exposure prediction model is trained per exposure and used to predict exposure-specific DMRs in the genome. Given these predicted exposure-specific DMRs, a set of unique DMRs per exposure can be identified. Analysis of these unique DMRs through visualization, DNA sequence motif matching, and gene association reveals known and unknown links between individual exposures and their unique effects on the genome. The results indicate the potential ability to define exposure-specific epigenetic markers in the genome and the potential relative impact of different exposures. Therefore, a computational approach to predict exposure-specific transgenerational epimutations was developed, which supported the exposure specificity of ancestral toxicant actions and provided epigenome information on the DMR sites predicted.

Bibliometric Study of Papers Published During 2007 to 2021

The study examines the change in pattern of impact factor and SCImago Journal Ranking (SJR) of the journal under study from 2008 to 2021 besides examining the pattern of growth of the number of papers. The study identified the most prolific actors (authors, institutions, and countries), besides examining their citation impact in terms of citation per paper & relative citation impact. The pattern of citation and highly cited papers have also been identified. Based on the analysis of data it is observed that the number of articles published was highest and almost equal in the years 2013, 2016, and 2017. Using the methodology of the complete count of records, it is observed that 56 countries contributed 2,939 articles. China followed by the USA published the highest number of papers. The value of CPP was highest for Universidad de Granada (Spain) and Leiden University (the Netherlands). Among the authors, Nees Jan van Eck and Ludo Waltman of the Leiden University had the highest CPP. Only a minuscule number of published articles remained uncited. Article authored by Aria, M. and Cuccurullo, C. published in issue 11(4), 2017, 959-975 of the journal received the highest number of citations. The pattern of authorship indicates that during the first ten years, more papers were single and two-authored while during 2017-2021 more number of papers were multi-authored.

Identification of Emotional Spectrums of Patients Taking an Erectile Dysfunction Medication: Ontology-Based Emotion Analysis of Patient Medication Reviews on Social Media.

Patient medication reviews on social networking sites provide valuable insights into the experiences and sentiments of individuals taking specific medications. Understanding the emotional spectrum expressed by patients can shed light on their overall satisfaction with medication treatment. This study aims to explore the emotions expressed by patients taking phosphodiesterase type 5 (PDE5) inhibitors and their impact on sentiment. This study aimed to (1) identify the distribution of 6 Parrot emotions in patient medication reviews across different patient characteristics and PDE5 inhibitors, (2) determine the relative impact of each emotion on the overall sentiment derived from the language expressed in each patient medication review while controlling for different patient characteristics and PDE5 inhibitors, and (3) assess the predictive power of the overall sentiment in explaining patient satisfaction with medication treatment. A data set of patient medication reviews for sildenafil, vardenafil, and tadalafil was collected from 3 popular social networking sites such as WebMD, Ask-a-Patient, and Drugs.com. The Parrot emotion model, which categorizes emotions into 6 primary classes (surprise, anger, love, joy, sadness, and fear), was used to analyze the emotional content of the reviews. Logistic regression and sentiment analysis techniques were used to examine the distribution of emotions across different patient characteristics and PDE5 inhibitors and to quantify their contribution to sentiment. The analysis included 3070 patient medication reviews. The most prevalent emotions expressed were joy and sadness, with joy being the most prevalent among positive emotions and sadness being the most prevalent among negative emotions. Emotion distributions varied across patient characteristics and PDE5 inhibitors. Regression analysis revealed that joy had the strongest positive impact on sentiment, while sadness had the most negative impact. The sentiment score derived from patient reviews significantly predicted patient satisfaction with medication treatment, explaining 19% of the variance (increase in R2) when controlling for patient characteristics and PDE5 inhibitors. This study provides valuable insights into the emotional experiences of patients taking PDE5 inhibitors. The findings highlight the importance of emotions in shaping patient sentiment and satisfaction with medication treatment. Understanding these emotional dynamics can aid health care providers in better addressing patient needs and improving overall patient care.