Individual Level Research Articles

Resective epilepsy surgery and its impact on depression in adults: a systematic review, meta-analysis, and implications for future research

BackgroundHow epilepsy surgery influences the bidirectional relationship of epilepsy and depression remains poorly defined.MethodFor a better understanding of this question, we conducted a systematic review and meta-analysis of risk ratio...

Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems

Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, at both the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.

Role of Social Media in COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) pandemic has produced a global health crisis that has had a deep impact on the way we perceive our world and everyday lives. Not only the spread rate of contagion and patterns of transmission endangered our sense of security, but the safety measures put in place to contain the spread of the virus also require social distancing by refraining from doing what is inherently human, which is to find comfort in the company of others. Within this context of physical threat, social and physical distancing, the role of the different mass media channels and social media in lives on individual, social and societal levels cannot be underestimated.

Alcohol use and its associated factors among Ethiopian military personnel

BackgroundStudies indicate that alcohol use is more prevalent in the military and that such use is related to coping mechanisms for stress. Alcohol use could result in health and social...

Risk factors determining adherence to tyrosine kinase inhibitors in chronic myeloid leukaemia.

Tyrosine kinase inhibitors (TKIs) have successfully changed the natural course of chronic myeloid leukaemia (CML). Although they are highly effective drugs, their clinical benefit is conditioned by adherence. This study aims to analyse the adherence of CML patients treated with TKIs and to identify the main factors influencing their adherence to TKIs treatment. An 8-month prospective, observational, multicentre study which included patients diagnosed with CML on treatment with TKIs attending the outpatient departments (OPD) of the Pharmacy Services of the participating hospitals. Adherence was assessed using two methods: the Simplified Medication Adherence Questionnaire (SMAQ) and the register of treatment dispensations from the OPDs. To analyse the predictors of adherence, a questionnaire was developed to report demographic and socio-economic information on the patients. A total of 130 patients enrolled in this study. Adherence rate was 56.9% (n = 74) among individuals, not conditioned by the type of drug used: imatinib (54.8%), nilotinib (63.6%) or dasatinib (54.3%) (p = 0.67). The patient educational level (p = 0.047) and employment status (p = 0.028) were predictors of non-adherence to treatment. Adherence is one of the most relevant parameters affecting the effectiveness of highly effective chronic treatments. Approximately half of our patients showed inadequate adherence to treatment with TKIs, with employment status and the individual's level of education emerging as the determining factors.

Leveraging Key Informant Interviews to Inform Intervention Development: The Greater Lawndale Healthy Work Project.

The Greater Lawndale Healthy Work project is a sequential mixed methods community based participatory research project that examines work as a structural determinant of health and builds community capacity for healthy work in a predominantly Black and Latinx community in Chicago known as Greater Lawndale (GL). We interviewed community leaders in GL as key informants to understand the barriers to healthy work and inform intervention development. We conducted a directed content analysis of transcripts from 20 key informants and coded the social ecology and type of intervention. Every key informant mentioned at least one asset in GL, showing an opportunity to employ a capacity-oriented approach to intervention development. Key informants suggested a variety of interventions to address precarious work across levels of the social ecology, with individual and community level interventions being the most salient. Through this approach, we were able to navigate tensions and challenges in conducting research for community-wide change. Key informant stakeholder interviews can be leveraged to meaningfully inform intervention development and support the development of multi-level, sustainable, and culturally acceptable interventions that advance health equity.

Health-related outcomes 6 months after integrated care of older people with stroke in 2 different primary care settings.

Integrated care has been proven to be effective among stroke survivors. However, in China, these services mainly focus on connecting the healthcare system (acute, primary medical, and skilled) at the individual level. Closer health and social care integration is a new concept. This study aimed to compare health-related outcomes 6 months after the implementation of the 2 integrated care models. It was a 6-month follow-up of an open, prospectively study comparing the outcomes of a model of integrated health and social care (IHSC) versus a usual integrated healthcare (IHC) model. Outcomes were measured by Short-Form Health Survey-36 (SF-36), Modified Barthel Index (MBI), and Caregiver Strain Index (CSI) at 3 and 6 months, respectively. There were no statistically significant differences in MBI scores between patients in the 2 models either after 3 months or at the end of intervention. The same trend was not seen in Physical Components Summary, an integral component of SF-36. Patients in IHSC model scored statistically significant higher points in Mental Components Summary, another integral part of SF-36 than patients in IHC model after 6 months. Average scores of CSI were statistically significant lower for IHSC model than for IHC model after 6 months. The findings suggest the need to improve the scales of integration and recognize the vital role played by social care services when designing or improving an integrated care for older people with stroke.

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

Analyzing the Energy Usage of a Community and the Benefits of Energy Storage

Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made it feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.

A “Think Coach, Think Male” Phenomenon: Women Coaches’ Experiences of Development, Progression, and Retention in Coaching Within the United Kingdom

Women coaches have remained underrepresented within sports coaching at all levels. Thus, the purpose of this paper was to describe the factors surrounding the development, progression, and retention of women coaches within the United Kingdom. The study involved semistructured interviews with 15 women coaches who were coaching female athletes in team sports. Content analysis of the qualitative data identified barriers and enablers at individual, interpersonal, organisational, and sociocultural levels. Results indicated that women coaches face several barriers because of their gender, which their men counterparts do not often encounter. Practically, individuals and organisations can both take action to reduce damaging stereotypical ideologies that produce challenging predicaments for women within the coaching landscape.

Multimodal Physiological Analysis of Impact of Emotion on Cognitive Control in VR.

Cognitive control is often perplexing to elucidate and can be easily influenced by emotions. Understanding the individual cognitive control level is crucial for enhancing VR interaction and designing adaptive and self-correcting VR/AR applications. Emotions can reallocate processing resources and influence cognitive control performance. However, current research has primarily emphasized the impact of emotional valence on cognitive control tasks, neglecting emotional arousal. In this study, we comprehensively investigate the influence of emotions on cognitive control based on the arousal-valence model. A total of 26 participants are recruited, inducing emotions through VR videos with high ecological validity and then performing related cognitive control tasks. Leveraging physiological data including EEG, HRV, and EDA, we employ classification techniques such as SVM, KNN, and deep learning to categorize cognitive control levels. The experiment results demonstrate that high-arousal emotions significantly enhance users' cognitive control abilities. Utilizing complementary information among multi-modal physiological signal features, we achieve an accuracy of 84.52% in distinguishing between high and low cognitive control. Additionally, time-frequency analysis results confirm the existence of neural patterns related to cognitive control, contributing to a better understanding of the neural mechanisms underlying cognitive control in VR. Our research indicates that physiological signals measured from both the central and autonomic nervous systems can be employed for cognitive control classification, paving the way for novel approaches to improve VR/AR interactions.

Do you cite what you tweet? Investigating the relationship between tweeting and citing research articles

Abstract The last decade of altmetrics research has demonstrated that altmetrics have a low to moderate correlation with citations, depending on the platform and the discipline, among other factors. Most past studies used academic works as their unit of analysis to determine whether the attention they received on Twitter was a good predictor of academic engagement. Our work revisits the relationship between tweets and citations where the tweet itself is the unit of analysis, and the question is to determine if, at the individual level, the act of tweeting an academic work can shed light on the likelihood of the act of citing that same work. We model this relationship by considering the research activity of the tweeter and its relationship to the tweeted work. The results show that tweeters are more likely to cite works affiliated with their same institution, works published in journals in which they also have published, and works in which they hold authorship. It finds that the older the academic age of a tweeter the less likely they are to cite what they tweet, though there is a positive relationship between citations and the number of works they have published and references they have accumulated over time.

Factors of Job Stress among university teachers in Pakistan A conceptual review

University teaching is universally known for its stressful nature. The university teachers are exposed to various stressors at work as a result of which their working performance is affected. The current study has conceptually reviewed the different factors of Job Stress among university teachers in Pakistan by using non-systematic narrative review technique. Following review of existing literature, the findings of current study revealed nine potential factors as determinants of Job Stress among university teachers in Pakistan. These factors were related to both intra and extra organizational environments of universities in Pakistan. This study concludes that university teaching is a stressful profession in Pakistan. The problem of Job Stress should be managed both at individual and institutional levels. Individually the academic staff members should get awareness about causes and consequences of Job Stress. At institutional level the management of universities should provide Stress free conducive environment, so that university teachers could be saved from the devastating effects of Job Stress.Â

Block Scheduling for LARC in a Family Medicine Residency Program.

Only 20% of family physicians report providing long-acting reversible contraception (LARC). Clinician-related barriers include confidence and comfort with LARC counseling and insertion/removal, and limited availability and uptake. Training during residency may address barriers and increase access/availability of LARC to support reproductive autonomy. We sought to determine the impact of block scheduling LARC clinics on resident comfort and confidence with LARC counseling and insertion/removal. LARC block schedules were established in a Midwest family medicine residency's primary clinic (FMC) and in a federally qualified health center rotation clinic. Baseline and end-of-study surveys, compared by Mann-Whitney U and Wilcoxon signed-rank tests, were used to assess comfort and confidence with counseling and inserting LARC. The number of LARC devices placed at the FMC were collected for the intervention year and the year prior. Twenty of 30 residents completed the baseline survey; 13 completed the end-of-study survey. At the group and individual levels, comfort increased for counseling on Levonorgestrel (LNG) intrauterine devices (IUDs) and for inserting implants and LNG IUDs. Individual comfort increased for copper IUDs. Resident willingness to recommend LARC increased, and more devices were placed during the intervention year than the year prior in the FMC (all: P<.05). Block scheduling of LARC clinics was associated with increased residents' comfort and confidence with counseling and placement of implants (LNG IUDs) and with an increase in LARCs placed at one clinic. Changes to scheduling may be an effective educational strategy that may increase access/availability to LARC.

Precision Education: The Future of Lifelong Learning in Medicine.

The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics , insights are generated to drive precision interventions . At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational levels, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.

Perceptual similarity judgments do not predict the distribution of errors in working memory.

Population coding models provide a quantitative account of visual working memory (VWM) retrieval errors with a plausible link to the response characteristics of sensory neurons. Recent work has provided an important new perspective linking population coding to variables of signal detection, including d-prime, and put forward a new hypothesis: that the distribution of recall errors on, for example, a color wheel, is a consequence of the psychological similarity between points in that stimulus space, such that the exponential-like psychophysical distance scaling function can fulfil the role of population tuning and obviate the need to fit a tuning width parameter to recall data. Using four different visual feature spaces, we measured psychophysical similarity and memory errors in the same participants. Our results revealed strong evidence for a common source of variability affecting similarity judgments and recall estimates but did not support any consistent relationship between psychophysical similarity functions and VWM errors. At the group level, the responsiveness functions obtained from the psychophysical similarity task diverged strongly from those that provided the best fit to working memory errors. At the individual level, we found convincing evidence against an association between observed and best-fitting similarity functions. Finally, our results show that the newly proposed exponential-like responsiveness function has in general no advantage over the canonical von Mises (circular normal) function assumed by previous population coding models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sentiment and Objectivity in Iranian State-Sponsored Propaganda on Twitter

In 2016, Russia attempted to use social media to influence the outcome of the U.S. presidential election, highlighting the potential real-world impacts of state-led online misinformation campaigns. Misinformation on social media is a growing concern, especially in the areas of politics and medicine, given their impact not only at the individual level but also for society as a whole. In this article, we investigate the potential to automatically label and detect the polarity (positive, neutral, or negative) of Iranian state-sponsored propaganda tweets on the Iranian nuclear deal. The SentiWordNet lexicon is used to automatically assign a polarity label and an objectivity score to each tweet. Using the labels, five machine learning algorithms are used to create polarity detection models. The experimental results show that the best performing models correctly identify polarity in approximately 77% of the tweets.

Recent Trends in Nanomaterial Based Electrochemical Sensors for Drug Detection: Considering Green Assessment.

An individual's therapeutic drug exposure level is directly linked to corresponding clinical effects. Rapid, sensitive, inexpensive, portable and reliable devices are needed for diagnosis related to drug exposure, treatment, and prognosis of diseases. Electrochemical sensors are useful for drug monitoring due to their high sensitivity and fast response time. Also, they can be combined with portable signal read-out devices for point-of-care applications. In recent years, nanomaterials such as carbon-based, carbon-metal nanocomposites, noble nanomaterials have been widely used to modify electrode surfaces due to their outstanding features including catalytic abilities, conductivity, chemical stability, biocompatibility for development of electrochemical sensors. This review paper presents the most recent advances about nanomaterials-based electrochemical sensors including the use of green assessment approach for detection of drugs including anticancer, antiviral, anti-inflammatory, and antibiotics covering the period from 2019 to 2023. The sensor characteristics such as analyte interactions, fabrication, sensitivity, and selectivity are also discussed. In addition, the current challenges and potential future directions of the field are highlighted.

The intersection of adverse childhood experiences and neighborhood determinants of health: An exploratory spatial analysis.

Adverse childhood experiences (ACEs) are associated with elevated risk for poor physical and psychological health outcomes. Nearly all of the literature on the association between ACEs and poor health focuses on the individual and family level; the potential role of neighborhood environment is overlooked. Understanding the relationship between ACEs and characteristics of the neighborhood environment is a necessary first step in determining if and how place-based, trauma-informed interventions might mitigate the negative effects of ACEs. The purpose of this exploratory study was to describe the neighborhood environment of adults who have experienced ≥ 4 versus ≤ 3 ACEs. An exploratory secondary analysis of cross-sectional and geospatial data was conducted during 2021. Data sources included 2011/2012 Philadelphia ACE Survey data, a telephone survey of 1,784 randomly sampled Philadelphia adults, linked with geospatial data on 21 neighborhood-level determinants of health. Neighborhood was defined as participant's home census tract at the time of survey. The sample for this secondary analysis included the 1,679 Philadelphia ACE Survey participants for whom home census tract was available. Bivariate logistic regression examined differences between groups (≤ 3 ACEs vs. ≥ 4 ACEs). Individuals with ≥ 4 ACEs lived in neighborhoods with higher neighborhood poverty rates, less socioeconomic resources, worse food access, poorer perceived physical and mental health, more substance overdose deaths, higher crime, and less green space. Findings suggest future work may benefit from considering neighborhood environments when examining and intervening upon the association between ACEs and poor physical and psychological health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Learner Assessment and Program Evaluation: Supporting Precision Education.

Precision education (PE) systematically leverages data and advanced analytics to inform educational interventions that, in turn, promote meaningful learner outcomes. PE does this by incorporating analytic results back into the education continuum through continuous feedback cycles. These data-informed sequences of planning, learning, assessing, and adjusting foster competence and adaptive expertise. PE cycles occur at individual (micro), program (meso), or system (macro) levels. This article focuses on program- and system-level PE.Data for PE come from a multitude of sources, including learner assessment and program evaluation. The authors describe the link between these data and the vital role evaluation plays in providing evidence of educational effectiveness. By including prior program evaluation research supporting this claim, the authors illustrate the link between training programs and patient outcomes. They also describe existing national reports providing feedback to programs and institutions, as well as 2 emerging, multiorganization program- and system-level PE efforts. The challenges encountered by those implementing PE and the continuing need to advance this work illuminate the necessity for increased cross-disciplinary collaborations and a national cross-organizational data-sharing effort.Finally, the authors propose practical approaches for funding a national initiative in PE as well as potential models for advancing the field of PE. Lessons learned from successes by others illustrate the promise of these recommendations.