Time Demands Research Articles

A Qualitative Study of School Mental Health Providers’ Experiences with Chart Notes: Perceived Utility, Burden, and Areas for Growth

ABSTRACT Background Despite the ubiquity of chart notes and their well-known impact on medical providers’ job stress and satisfaction, little is known about how mental health providers interact with and are impacted by documentation requirements, particularly in school environments. Objective This qualitative study sought to investigate school mental health providers’ perspectives on and experiences with documentation. Method Four focus groups with eight school mental health providers were conducted. Providers were asked about their current documentation practices and desired changes to their electronic health record system. Recordings of focus groups were transcribed and analyzed for themes using a rapid qualitative analytic approach. Results Key themes revolved around the purpose, features, time demands, training needs, and writing processes surrounding chart notes. Providers reported spending substantial time and energy on documentation and viewed chart notes primarily as a billing requirement, but also valued their utility for treatment planning, outcome monitoring, and clinical accountability. Providers expressed frustration with redundant data entry, lack of protected time for documentation, insufficient training for and communication around updates to documentation requirements, and strained interactions with supervisors during editing. Providers highlighted scheduling protected work time for documentation, enhancing documentation training with a focus on habit formation, standardizing supervisory expectations and providing documentation specific training for supervisors, and redesigning electronic health record interfaces with automation and checklists as strategies that could improve their experiences with chart notes. Conclusions Providers highlighted the ways current training and supervision practices, time constraints, and electronic health record functionality contribute to job stress and burnout while also noting the value of documentation for treatment planning and processing difficult emotions. Roadmaps outlining further research that is needed to understand these suggestions from providers in the larger context of clinical documentation, as well their potential benefits and tradeoffs for providers and other stakeholders, are discussed.

Adversity and Resiliency: Athlete Experiences within U.S. College Sport

Research regarding the benefits and detriments of the U.S. collegiate sport governance structure are mixed. Guided by Richardson’s Resiliency Theory (2002), former athletes (n = 215) revealed specific themes of adversity experienced during college with the most prevalent including injury, time demands, and coach-athlete tension. Through interview, athletes noted adversity promoted their resiliency, facilitated grit/perseverance, enhanced teamwork and time management skills, and led to other forms of growth. This study extends our understanding of the long-term impacts of competitive sport participation in this context. This understanding is important for administrators seeking to maximize participant experiences and emulate, enhance, or reform the U.S. College Sport governance model.

Preparing PhD Students for Tenure-Track Faculty Positions.

With an increasing number of nursing faculty approaching retirement, it is critical to prepare PhD students to take on tenure-track faculty roles. There are a multitude of competing time demands for nursing faculty, and one way that PhD students can prepare for faculty roles is leading team-based science with their peers during their predoctoral program. To describe the process of PhD students leading an independent research project, in addition to their dissertation work, which aimed to incorporate the tripartite mission- scholarship, teaching, and service. This paper outlines the components of the PhD-student-led project, as well as provides recommendations for leadership and schools of nursing as well as sponsors interested in funding this type of work. Students were able to conduct independent research using diverse research approaches, practice mentoring skills, and provide service and helpful information about the nursing workforce to their academic clinical partner. A key component of this relationship building was incorporating a clinical fellowship during the PhD program for students that directly aided the transition from BSN to PhD programs. PhD students learned valuable lessons about time management and collaborating in a team setting with peers. We recommend school of nursing seek funding opportunities for PhD students to conduct independent team science, encourage academic partnerships with affiliated medical centers and public health settings, and facilitate peer mentorship across cohorts.

An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems

Detached eclipsing binary (EB) systems are crucial for measuring the physical properties of stars that evolve independently. Large-scale time-domain surveys have released a substantial number of light curves for detached EBs. Utilizing the Physics of Eclipsing Binaries package in conjunction with Markov Chain Monte Carlo (MCMC) methods for batch parameter derivation poses significant computational challenges, primarily due to the high computational cost and time demands. Therefore, this paper develops an efficient method based on the neural network model and the stochastic variational inference method (denoted NNSVI) for the rapid derivation of parameters for detached EBs. For studies involving more than three systems, the NNSVI method significantly outperforms techniques that combine MCMC methods in terms of parameter inference speed, making it highly suitable for the batch derivation of large numbers of light curves. We efficiently derived parameters for 34,907 detached EBs, selected from the Optical Gravitational Lensing Experiment catalog and located in the Galactic bulge, using the NNSVI method. A catalog detailing the parameters of these systems is provided. Additionally, we compared the parameters of two double-lined detached EBs with those from previous studies and found the estimated parameters to be essentially identical.

Co-Designing Dynamic Mixed Reality Drill Positioning Widgets: A Collaborative Approach with Dentists in a Realistic Setup.

Mixed Reality (MR) is proven in the literature to support precise spatial dental drill positioning by superimposing 3D widgets. Despite this, the related knowledge about widget's visual design and interactive user feedback is still limited. Therefore, this study is contributed to by co-designed MR drill tool positioning widgets with two expert dentists and three MR experts. The results of co-design are two static widgets (SWs): a simple entry point, a target axis, and two dynamic widgets (DWs), variants of dynamic error visualization with and without a target axis (DWTA and DWEP). We evaluated the co-designed widgets in a virtual reality simulation supported by a realistic setup with a tracked phantom patient, a virtual magnifying loupe, and a dentist's foot pedal. The user study involved 35 dentists with various backgrounds and years of experience. The findings demonstrated significant results; DWs outperform SWs in positional and rotational precision, especially with younger generations and subjects with gaming experiences. The user preference remains for DWs (19) instead of SWs (16). However, findings indicated that the precision positively correlates with the time trade-off. The post-experience questionnaire (NASA-TLX) showed that DWs increase mental and physical demand, effort, and frustration more than SWs. Comparisons between DWEP and DWTA show that the DW's complexity level influences time, physical and mental demands. The DWs are extensible to diverse medical and industrial scenarios that demand precision.

Grab the Mic: Connecting theory and practice within an undergraduate sport communication course

Experiential learning within sport management classrooms provides valuable industry experience to complement theoretical knowledge. Content creation is a valuable skill for sport management students, particularly those who are looking to work within media and marketing spaces after graduation. Therefore, sport communication undergraduate students at a large research-intensive institution in the Midwest of the United States participated in a podcast creation project for their sport communication class. The experiential learning opportunity provided students with the opportunity to collaborate with a classmate to write, record, and edit a podcast episode under the umbrella of the department's podcast show. This gave the students valuable experience in creating content for professional consumption. The project received positive feedback from students on skill development and the benefits of collaboration, while also discussing the challenges associated with the time demands of the project.

Barriers and facilitators of implementing a health literacy-based intervention in Portugal

Abstract Background Despite the effectiveness of cervical cancer screening (CCS) in reducing mortality rates, disparities persist in access to screening among women with low health literacy, often termed ‘hard-to-reach women’. Health literacy-based interventions can help promote screening uptake. Based on a health literacy-based intervention for CCS implemented in Lisbon with the support of community workers, this study explores barriers and facilitators of the implementation process as perceived by these actors. Methods The intervention consisted of educational sessions on cervical cancer and CCS aimed at 246 women, implemented in community settings in 2023-2024. Within a mixed-methods approach, a pre-and post-intervention survey was applied to assess willingness to screen, and two focus groups were conducted with 10 community workers on their implementation experiences. Descriptive analysis and content analysis were performed where appropriate. Results Of the 665 women invited, 37% were enrolled in the sessions; 91% completed the follow-up. Community workers valued the informative content as adequate to raise awareness. Implementation facilitators included women’s trust in collaborating organisations and a complement of peer recruitment. Challenges included the additional workload of community workers, finding creative strategies to engage women, scheduling the sessions that best suited women’s time demands, and limited spaces to accommodate several group sessions. Proposed solutions to overcome barriers included increasing the time availability of community workers to support participants’ recruitment and session delivery, conducting itinerant and open sessions, and integrating the sessions within the organisation’s health-promoting activities. Conclusions Health literacy-based interventions are helpful in raising CCS awareness. Tailored interventions sensitive to context demands may contribute to successful actions. Key messages • Health literacy interventions can be key to promoting cervical cancer screening among hard-to-reach women. • Tailored community-based interventions may help address barriers to cervical cancer screening.

Multi-receptive-field physics-informed neural network for complex electromagnetic media

Acquiring the electromagnetic response of intricate media at the nanoscale constitutes a pivotal phase in the design intricacies of nanophotonic apparatuses. Conventional numerical algorithms often necessitate intricate and specialized treatments to accommodate the unique properties of the medium, coupled with substantial computational time and resource demands. In recent years, the advent of deep learning technology has heralded numerous advancements in the domain of computational electromagnetics, albeit with a scarcity of solvers tailored for versatile complex media. Consequently, this study introduces an innovative multi-receptive-field physics-informed neural network (MRF-PINN) designed to tackle nano optical scattering predicaments inherent in media exhibiting dispersion, inhomogeneity, anisotropy, nonlinearity, and chirality. This framework adeptly captures electromagnetic perturbations surrounding scatterers via variable-scale receptive fields, thereby enhancing prediction precision. Within the training regimen, a scale balancing algorithm is proposed to expedite network convergence. Empirical findings demonstrate that a fully trained MRF-PINN proficiently reconstructs electromagnetic field distributions within complex nanomaterials within a mere tens of milliseconds of inference time. Such quasi real-time capabilities herald a novel approach to supplant the arduous forward calculation processes inherent in nanomaterial design workflows.

IgE immunoadsorption: technical background, functionality, and first clinical experience

Summary Background The prevalence of allergic diseases has risen in the 21st century, drawing attention to specific therapeutic and preventive strategies. Due to the key role of immunoglobulin E (IgE) in the development of allergic reactions, IgE represents a key target treatment. In this scenario, IgE immunoadsorption (IgE-IA) has been investigated as a procedure that selectively removes circulating IgE antibodies from the bloodstream of patients with atopy. Methods This narrative review aims to critically summarize the current insights regarding IgE-IA in the context of the management of allergic diseases, ranging from the rationale to the technical aspects, as well as the benefits and unmet needs. Results IgE-IA might be a treatment strategy in well-selected patients with allergic diseases. IgE depletion through sessions of IgE-IA results in immediate clinical improvement and might be useful in acute situations when a rapid clinical response is required or when classic approaches are contraindicated or ineffective. Due to the reduced effectiveness over time, IgE-IA could be a valid first approach before starting another IgE depletion therapy, such as omalizumab, when its commencement would otherwise be contraindicated by too-high serum IgE levels. Conclusion Overall, IgE-IA is safe and well tolerated; however, this procedure is currently difficult to implement in routine clinical practice because of costs, time demands, need for hospitalization, and the invasiveness of the procedure, with the associated risks related to the necessity of venous catheterization.

Structural characterization of a suspension bridge by mapping the temperature effects on strain response based on neural network models

AbstractMapping the structural responses based on main loads to characterize signature of complex structures with high-dimensional features is a determinant factor for structural health monitoring (SHM). Current technological advances contribute to the optimization of data analysis, aiming to make the process less demanding in terms of time and computational demand. Machine learning (ML) models became popular due to their capacity to estimate structural behaviour based on the measurements gathered by the SHM systems. This work proposes a methodology supported by Neural Networks (NN) for the characterization and prediction of the structural behaviour based on thermal loads and structural responses. By comparing the observed values and predicted outcomes from the NN, it is possible to identify measuring errors, new trends or pattern variations that need further assessment. A sensitivity analysis is also proposed to confirm the model robustness and to characterize the influence of the temperature on the structural responses. The case study is the 25 de Abril’s bridge, located in Lisbon, Portugal.

Re-examining ‘personalised conditionality’: full-time obligations, partial adjustments and power asymmetries in the UK’s approach to work-related conditionality

Abstract Work-related conditionality policy in the UK is built around the problematic assumption that people should commit to ‘full-time’ work and job search efforts as a condition of receiving benefits. This is potentially in conflict with the idea that what is required of people should be tailored to their circumstances in some way – ‘personalised conditionality’ – and implies a failure to recognise that conditionality is being applied to a diverse group of people and in a context where the paid work that is available is often temporary and insecure. Drawing on thirty-three qualitative interviews with people subject to intensive work-related conditionality whilst receiving Universal Credit or Jobseeker’s Allowance in Manchester, the paper explores the work-related time demands that people were facing and argues that these provide a lens for examining the rigidities and contradictions of conditionality policy. The findings indicate that expectations are often set in relation to an ideal of full-time hours and in a highly asymmetric context that is far from conducive to being able to negotiate a reasonable set of work-related expectations. Work search requirements affect people differently depending on their personal circumstances and demand-side factors, and can act to weaken the position of people entering, or already in, work.

Evaluating a peer-support mind-body medicine intervention for healthcare leaders.

Mind-body medicine (MBM) is an evidence-based intervention associated with trauma and stressful events. The MBM intervention alleviates symptoms of work-related stress and builds resilience by utilizing self-care techniques facilitated in small group settings. Healthcare leaders who experienced traumatic stress through the COVID-19 pandemic may benefit from interventions aimed at their needs. We evaluated the effects of a peer support MBM intervention on perceived stress, resilience, well-being, and empathy for nurse leaders and compassionate care leaders. A pre-post intervention was conducted via 7 virtual and 2 on-site groups, recruiting from a large multihospital health system in the United States. Participants engaged in an 8-week program facilitated by a certified faculty group leader from The Center for Mind-Body Medicine and engaged in resilience skills building activities along with facilitated sharing. Validated instruments were used to measure outcomes at pre, post, 1 month, and 6 month follow-up intervals. Surveys included open-ended questions for qualitative feedback related to facilitators, barriers, and group experiences. Seventy-three leaders completed the MBM program, and 22 completed the four research surveys; all qualitative responses were included for feedback. Perceived stress decreased after the intervention (p < .008) and was maintained for 6 months post intervention (p < .005). Resilience increased after the intervention (p < .034) and for 1 month (p < .049) but decreased after 6 months. Qualitative responses showed that time and workload factors were the most significant barrier to participation, while the benefits included protected time with peers, learning well-being skills, and having a safe place to process emotions. Healthcare leaders face unique challenges, including workplace trauma and crises. Interventions that support their stress response, resilience, and overall well-being should take into consideration the nature of their work, the balance of time demands, and the need for peer support to overcome barriers to sustainable interventions.

Perspectives of Bilingual Training of Lawyers in Russia: The Demands of Time and Society

Perspectives of Bilingual Training of Lawyers in Russia: The Demands of Time and Society

Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.

Conventional genomic selection models trait individually, neglecting complex trait interactions. Multi-trait models address this by considering genetic correlations, thus improving breeding value accuracy. Despite their theoretical benefits, quantifying these models' breeding advantages across genetic backgrounds is essential. This study evaluates the benefits of multi-trait models under varying population sizes and three levels of genetic correlations (low, medium, high) using simulations based on 50 K chip data from 5000 individuals. In equal heritability scenarios, the multi-trait GBLUP model consistently outperforms single-trait models, with breeding advantages increasing with heritability. For example, with a reference population of 4500, improvements range from 0.3% to 4.1%. Notably, trait combinations with low heritability are insensitive to changes in genetic correlation, with gains remaining ≤ 0.1% across different genetic correlations under low heritability conditions. In differing heritability scenarios, the multi-trait model's benefits vary, particularly enhancing low-heritability traits when paired with high-heritability ones. Additionally, modeling time increases as genetic correlation decreases. The results of this study indicate that multi-trait models improve breeding accuracy but require more modeling time and place higher demands on algorithms and software. We recommend breeding strategies tailored to different phenotypes and genetic backgrounds to balance efficiency and accuracy.

Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm

Using UAV-based RGB images to recognize maize seedlings is of great significant for precise weed control, efficient water and fertilizer management. However, the presence of weeds with morphological resemblances at the maize seedling stage affects the recognition of maize seedlings. This research employs UAV RGB images and deep learning algorithms to achieve accurate recognition of maize seedlings under weed disturbance. Firstly, the adaptive anchor frame algorithm is employed to intelligently select optimal anchor frame sizes suited for the maize seedling from UAV images. This strategic selection minimizes time and computational demands associated with multiple anchor frame sampling. Subsequently, the Global Attention Mechanism (GAM) is introduced, bolstering feature extraction capabilities. A range of deep learning models, including YOLOv3 and YOLOv5, are applied for maize seedling recognition, culminating in the identification of an optimal model. To account for real-world scenarios, we investigate the influences of UAV flight height and weed disturbance on maize seedling recognition. The results indicate a multi-class Average Precision (mAP) of 94.5 % and 88.2 % for detecting maize seedlings at flight heights of 15 m and 30 m, respectively, with an average detection speed of 0.025 s per single image. This emphasizes the efficacy of the improved YOLOv5 deep learning model in recognizing maize seedlings under weed disturbance using UAV RGB images.

Exploratory mixed methods investigation of time commitment in youth sport and physical activity: families’ experiences and satisfaction during the pandemic, and intentions for post-pandemic engagement

ABSTRACT Purpose/rationale: The COVID-19 pandemic drastically changed families’ time commitments for youth sport and physical activity (SPA). Guided by Social Ecology Theory, experiences and associated levels of satisfaction with these changes are explored, as well as intentions for post-pandemic engagement. Design/methodology/approach: Interviews with families (n = 14) informed the creation of a questionnaire that was distributed to 550 Ontarian families. Findings: Parents were satisfied with lower time commitment of youth SPA during the pandemic and would have liked to engage in less travel for youth SPA and fewer organized activities post-pandemic. However, families largely intended on increasing or reverting to their travel time for and diversity of activities post-pandemic, compared to pre-pandemic, suggesting that parents intend to support their children’s SPA preferences, regardless of their own. Practical implications: Given that parents intend to support their children’s SPA commitment preferences, tactics to ease the time demands on parents can be developed to provide greater value propositions for families. Research contribution: Two main consumer groups of youth SPA (i.e. parents and youth) can have differing preferences for their experiences. Originality/value: This study revealed conflicting experiences and preferences of two distinct consumer groups for youth SPA.

Frustrations with time spent on cancer care among patients with metastatic breast or advanced ovarian cancer.

235 Background: Individuals with cancer face substantial demands on their energy and time due to the complexity of cancer care. We sought to describe frustrations with time-consuming aspects of cancer care among individuals with advanced ovarian or breast cancer. We hypothesized that frustrations would be highest for time spent on administrative tasks, particularly for those who are younger, employed, and have dependents. Methods: We analyzed baseline survey data from an ongoing prospective study of the time burdens of cancer care among individuals receiving treatment for ovarian or metastatic breast cancer at the University of Minnesota and University of Alabama-Birmingham. The survey included questions about frustrations with the time needed for cancer-related activities such as travel time, finding transportation, care coordination, receiving treatment, and paperwork (0=Not at all, 10=Extremely frustrated). We summarized the data using descriptive statistics and made comparisons across demographic and clinical characteristics using Wilcoxon rank-sum and Kruskal-Wallis tests. Results: A total of 67 participants completed the baseline survey, 37 (55.2%) with metastatic breast cancer and 30 (44.8%) with ovarian cancer. About 1/4 of participants (26.9%) were under 50 years old, 34.3% had dependents, 39.1% were employed, and 57.6% lived 30+ miles from the cancer clinic; 73.1% were non-Hispanic (NH) White and 22.4% NH Black; 4.5% reported another race/ethnicity. Over half (58.2%) agreed minimizing time spent on cancer-related activities was important to them; those employed were significantly more likely to agree (76.0% vs. 48.7%, p=0.03). Activities with the highest reported frustrations were the number of trips, time for care-related travel or paperwork, and wait time. Younger participants reported higher frustration with wait time (p=0.04) and time at the infusion center than older participants (p=0.03). Employed participants reported significant frustration with having to take time off work (p&lt;0.0001), and those with dependents reported frustration needing to organize dependent care (p=0.01). Participants living 30+ miles from the cancer clinic reported greater frustrations with travel time (p=0.01), time needed for care coordination (p=0.01) and waiting for the provider (p=0.02). Some (10.6%) participants reported lacking reliable transportation and, among those, reported high frustration with the time needed for numerous activities, including finding transportation, paperwork, care coordination, and making appointments (all p&lt;0.05). Conclusions: Cancer-related time demands are frustrating for many patients. Those employed, younger, with dependents, living further from the cancer clinic, and without reliable transportation experience greatest frustrations about time lost, underscoring the need to address these often unrecognized aspects of care.

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5σ point-source depth of g= 24.3, r=23.9, i= 23.5, and z= 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of −0.0013, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

Accelerated Detection of Foodborne Pathogens and Toxins: Techniques and Applications – A Review

Pathogens associated with food and their corresponding toxins, including mycotoxins, pose a sig¬nificant threat to public health, leading to various health issues worldwide, particularly affecting infants, young children, and the elderly. The rise in foodborne illnesses highlights the crucial importance of food safety in ensuring a reliable food supply and promoting health. Timely detection of foodborne pathogens, toxins, and mycotoxins is essential for reducing the incidence of foodborne diseases. This review aims to provide comprehensive insights into the methodology for rapid detection, detailing the principles, appli¬cations, advantages, and limitations to enhance current knowledge. Conventional culture-based methods for foodborne pathogen identification are selective and are hindered by significant time demands, labor intensity, challenges from complex sample preparation, delayed results, and the need for specialized per¬sonnel. In contrast, the new emerging detection techniques such as immunological, nucleic acid-based, and biosensor-based methods offer time efficiency, reduced labor, improved accuracy, sensitivity, specificity, and reliability, and are user-friendly. Recently, numerous novel techniques have been developed for the rapid identification of foodborne pathogens, toxins, and mycotoxins, transforming food safety practices. Providing rapid and accurate results is crucial for controlling and managing foodborne disease outbreaks while ensuring food safety. As the global food supply chain becomes more complex, advancing rapid and automated detection methods remains essential for safeguarding food safety and quality, as well as enhanc¬ing public health.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.