Total Workload Research Articles

Estimation of House Cleanup Work Volume Based on Disaster Volunteer Center Work Management Data —The Case of the 2015 Joso City—

To understand the workload of house cleanup and related workforce shortage after a disaster, the actual work situation in disaster-stricken areas is accounted for by disaster volunteer work management data created by the Disaster Volunteer Center of Joso City in Ibaraki Prefecture at the time of the Kanto–Tohoku Heavy Rain Disaster in September 2015. Using the classification of inundation depth, judged from ground elevation, the weekly workload of house cleanup according to the work content is recorded to clarify the characteristics of each area. Comparing this with the inundated areas without destructions by water flow, near the bank break with house destructions, in the urban area, and around the farmland along the old road, a model to estimate the workload is constructed. It was observed that indoor work to recommence living in urban areas continued for a long time, while the work was completed in a relatively short time in the area along the old road. The area near the bank break, with a small number of houses, witnessed very few house destructions. Hence, it is not necessary to separately calculate the workload caused by house destructions. The appropriateness of the estimated results was verified by using a method to estimate the workload based on the amount of disaster waste. As a result, the total workload estimated by disaster volunteers and victims for the busy period of two months was a million people. In the case of the Joso City flood, very few houses were completely destroyed, therefore, regular living could be resumed swiftly and people settled there after the disaster due to its proximity to the metropolitan area. Hence, the population decreased by the flood was recovered in two years.

Relationship Between Job Stressor with Job Stress on Salesmen of Household Appliance Trading

Introduction: Salesmen’s job is focused on promoting a product directly to consumers. Household appliances are one of the products that is often promoted by salesmen. While carrying out marketing activities, they will always be faced with various job factors that can cause job stress. This research was conducted to analyze the correlation between job stressors and job stress levels among salesmen. Method: This research was observational research with a cross-sectional approach. Both population and samples of this research were 30 salesmen of household appliance trading in Kediri City. The sampling technique used was total sampling. Variables used in this research were job stressor including role conflict, role ambiguity, interpersonal conflict, job uncertainty, total workload, and mental demands with job stress levels. Results: The results showed that most salesmen experienced job stressors either in high, moderate or low category. Most of the salesmen experienced normal job stress level. There is relationship between job stressors consisting of role conflict (0.673), role ambiguity (0.455), interpersonal conflict (0.262), job uncertainty (0.683), total workload (0.526) and mental demands (0.859) with job stress levels on salesmen. Conclusion: An increase in job stressor conditions experienced by salesmen will be followed by an increase in job stress levels, and vice versa. Out of all, mental demands have the strongest relationship to job stress levels on salesmen. So it is necessary to make efforts to suppress existing job stressors so that an increase of job stress levels on salesmen can be prevented.

Characterization of Female US Marine Recruits: Workload, Caloric Expenditure, Fitness, Injury Rates, and Menstrual Cycle Disruption during Bootcamp.

Basic training is centered on developing the physical and tactical skills essential to train a recruit into a Marine. The abrupt increase in activity and energy expenditure in young recruits may contribute to high rates of musculoskeletal injuries, to which females are more susceptible. To date, the total workload of United State Marine Corps (USMC) bootcamp is unknown and should include movement around the military base (e.g., to and from dining facilities, training locations, and classrooms). Thus, the purpose of this effort was to quantify workload and caloric expenditure, as well as qualitatively assess the impact of female reproductive health and injury rates in female recruits. Female recruits (n = 79; age: 19.1 ± 0.2 years, weight: 59.6 ± 0.8 kg, height: 161.6 ± 0.7 cm) wore physiological monitors daily throughout 10 weeks of USMC bootcamp. Physical fitness test scores, physiological metrics from wearables, injury data, and menstrual cycle information were obtained. Female recruits on average expended 3096 ± 9 kcal per day, walked 11.0 ± 0.1 miles per day, and slept 5:43 ± 1:06 h:min per night throughout the 10 weeks of bootcamp. About one-third (35%) of female recruits sustained an injury. In a subset of females that were not taking birth control and had previously been menstruating, 85% experienced cycle dysfunction during boot camp. High levels of physical activity and caloric expenditure, coupled with the stress of a new environment and insufficient sleep, may lead to alterations in female reproductive cycles and musculoskeletal injuries in young USMC recruits.

Workload and use factor data for a modern digital radiography system.

The well-referenced structural shielding design NCRP Report No. 147 uses workload information based on self-reported film-screen data from the AAPM Task Group 9 survey. The aim of this study was to assess the clinical workload distributions of modern digital radiography (DR) systems in general hospital and pediatric-only practices. A retrospective analysis of DR imaging data on four radiographic systems in a hospital practice and two radiographic systems in a pediatric practice, through a custom clinical DICOM header analytics program. A total of 203, 294 exposures from the general hospital practice and 25,415 from the pediatric practice from 2019 and 2021 were included. Values for kVp, mAs, and detector type (wall bucky, table bucky, or free detector) were extracted. For each exam, mAs was accumulated in a kVp histogram with bins 5kVp wide and further parsed by detector type. Total workload was calculated by summing all exposures, then normalized by the number of patients. The median (25th and 75th percentile) workload in the hospital practice was 0.43 (0.22, 1.13)mA-min per patient, while the average was 1.36±3.08. Pediatric data yielded a median (25th and 75th percentile) of 0.10 (0.05, 0.23) and an average of 0.29±0.69mA-min per patient. Mean number of patients per week was 230 adult and 57 pediatric. Hospital workload data is approximately 44% less than the NCRP Report No. 147 value.

Multi-objective home health care routing: a variable neighborhood search method.

Health and convenience are two indispensable indicators of the society promotion. Nowadays, to improve community health levels, the comfort of patients and those in need of health services has received much attention. Providing Home Health Care (HHC) services is one of the critical issues of health care to improve the patient convenience. However, manual nurse planning which is still performed in many HHC institutes results in a waste of time, cost, and ultimately lower efficiency. In this research, a multi-objective mixed-integer model is presented for home health care planning so that in addition to focusing on the financial goals of the institution, other objectives that can help increase productivity and quality of services are highlighted. Therefore, four different objectives of the total cost, environmental emission, workload balance, and service quality are addressed. Taking into account medical staff with different service levels, and the preferences of patients in selecting a service level, as well as different vehicle types, are other aspects discussed in this model. The epsilon-constraint method is implemented in CPLEX to solve small-size instances. Moreover, a Multi-Objective Variable Neighborhood Search (MOVNS) composed of nine local neighborhood moves is developed to solve the practical-size instances. The results of the MOVNS are compared with the epsilon-constraint method, and the strengths and weaknesses of the proposed algorithm are demonstrated by a comprehensive sensitivity analysis. To show the applicability of the algorithm, a real example is designed based on a case study, and the results of the algorithm over real data are evaluated.

Validation and effect of the NASA-TLX score on the assessment of the workload of pediatric robotic operations.

The objective of the study was to assess the validity of the NASA-TLX score in rating the workload of pediatric robotic operations. The workload of 230 pediatric gastrointestinal and thoracic robotic operations was rated using the NASA-TLX score. The difference between the high workload group and the low workload group in each subscale of the NASA-TLX score was analyzed. The correlation of each subscale with the total workload score in the high workload group and low workload group was also analyzed. A logistic regression analysis was subsequently conducted to assess the effects of different factors (sex, age, weight, procedure duration, procedure specialties, combined malformation and blood loss) on the workload. The average NASA-TLX score was 56.5 ± 5.1 for the total group, 56.9 ± 5.0 for the gastrointestinal group and 54.6 ± 4.8 for the thoracic group, p = 0.007. The score of the high workload group was 62.7 ± 3.2, while it was 50.6 ± 2.7 for the low workload group (p < 0.001). The score on each subscale was also significantly different between the high and low workload groups. In the high workload group, a stronger correlation was observed between the total score and TD and Fr and a lower correlation with MD and Pe. In the low workload group, all six subscales showed a moderate correlation with the total score. A multivariate logistic regression analysis revealed that the procedure duration was an independent influencing factor for a higher workload score. NASA-TLX is a valid tool to rate the surgeon's workload in pediatric robotic surgery. A longer operative time contributes to a higher workload.

Workflow interruption and nurses’ mental workload in electronic health record tasks: An observational study

BackgroundWorkflow interruptions are common in modern work systems. Electronic health record (EHR) tasks are typical tasks involving human–machine interactions in nursing care, but few studies have examined interruptions and nurses’ mental workload in the tasks. Therefore, this study aims to investigate how frequent interruptions and multilevel factors affect nurses’ mental workload and performance in EHR tasks.MethodsA prospective observational study was conducted in a tertiary hospital providing specialist and sub-specialist care from June 1st to October 31st, 2021. An observer documented nurses’ EHR task interruptions, reactions and performance (errors and near errors) during one-shift observation sessions. Questionnaires were administered at the end of the electronic health record task observation to measure nurses’ mental workload for the electronic health record tasks, task difficulty, system usability, professional experience, professional competency, and self-efficacy. Path analysis was used to test a hypothetical model.ResultsIn 145 shift observations, 2871 interruptions occurred, and the mean task duration was 84.69 (SD 56.68) minutes per shift. The incidence of error or near error was 158, while 68.35% of errors were self-corrected. The total mean mental workload level was 44.57 (SD 14.08). A path analysis model with adequate fit indices is presented. There was a relationship among concurrent multitasking, task switching and task time. Task time, task difficulty and system usability had direct effects on mental workload. Task performance was influenced by mental workload and professional title. Negative affect mediated the path from task performance to mental workload.ConclusionsNursing interruptions occur frequently in EHR tasks, come from different sources and may lead to elevated mental workload and negative outcomes. By exploring the variables related to mental workload and performance, we offer a new perspective on quality improvement strategies. Reducing harmful interruptions to decrease task time can avoid negative outcomes. Training nurses to cope with interruptions and improve competency in EHR implementation and task operation has the potential to decrease nurses’ mental workload and improve task performance. Moreover, improving system usability is beneficial to nurses to mitigate mental workload.

Dynamic performance analysis of workover power tong and boom based on AMESim

In workover operations, minor workover operations account for about 90%~95% of the total wellbore maintenance workload, that is, in oilfield workover operations, the longest operation time and the highest use frequency is the tripping operation of pumping tubing and sucker rod [1]. According to statistics, the time spent in tripping operation of oil wells generally accounts for nearly three quarters of the total time spent in workover [2]. Therefore, it is very necessary to improve the efficiency of tripping operation and realize the automation of tripping operation of tubing and rod for improving the mechanization level of oil fields in China and maintaining stable production of oil fields [3]. In the lifting operation engineering, the most important part is the automatic make-up and breakout link. The efficiency and quality of make-up and breakout are determined by the power tong, so it is particularly important to study the dynamic performance of the power tong. In this paper, amesim is used to establish the hydraulic control circuit of the power tong, and the optimal control scheme is obtained by analyzing the performance curve of the power tong under different pressures [4].

Assembly and Production Line Designing, Balancing and Scheduling with Inaccurate Data: A Survey and Perspectives

Assembly lines (conveyors) are traditional means of large-scale and mass-scale productions. An assembly line balancing problem is needed for optimizing the assembly process by configuring and designing an assembly line for the same or similar types of final products. This problem consists of designing the assembly line and distributing the total workload for manufacturing each unit of the fixed product to be assembled among the ordered workstations along the constructed assembly line. The assembly line balancing research is focused mainly on simple assembly line balancing problems, which are restricted by a set of conditions making a considered assembly line ideal for research. A lot of published research has been carried out in order to describe and solve (usually heuristically) more realistic generalized assembly line balancing problems. Assembly line designing, balancing and scheduling problems with not deterministic (stochastic, fuzzy or uncertain) parameters have been investigated in many published research works. This paper is about the design and optimization methods for assembly and disassembly lines. We survey the recent developments for designing, balancing and scheduling assembly (disassembly) lines. New formulations of simple assembly line balancing problems are presented in order to take into account modifications and uncertainties characterized by real assembly productions.

An improved ant colony optimization for solving the flexible job shop scheduling problem with multiple time constraints.

The flexible job shop scheduling problem is important in many research fields such as production management and combinatorial optimization, and it contains sub-problems of machine assignment and operation sequencing. In this paper, we study a many-objective FJSP (MaOFJSP) with multiple time constraints on setup time, transportation time and delivery time, with the objective of minimizing the maximum completion time, the total workload, the workload of critical machine and penalties of earliness/tardiness. Based on the given problem, an improved ant colony optimization is proposed to solve the problem. A distributed coding approach is proposed by the problem features. Three initialization methods are proposed to improve the quality and diversity of the initial solutions. The front end of the algorithm is designed to iteratively update the machine assignment to search for different neighborhoods. Then the improved ant colony optimization is used for local search of the neighborhood. For the searched scheduling set the entropy weight method and non-dominated sorting are used for filtering. Then mutation and closeness operations are proposed to improve the diversity of the solutions. The algorithm was evaluated through experiments based on 28 benchmark instances. The experimental results show that the algorithm can effectively solve the MaOFJSP problem.

Fear of making a mistake: a prominent cause of stress for COVID-19 ICU staff—a mixed-methods study

IntroductionThe COVID-19 pandemic has had a profound effect on many domains of healthcare. Even in high-income countries such as Sweden, the number of patients has vastly outnumbered the resources in...

Changes in consultation mode during different phases of the COVID-19 pandemic in Croatian family medicine: a cross-sectional study

ObjectivesTo describe the changes in the type, length and reasons for consultations in primary healthcare during the COVID-19 pandemic in Croatia. This study aimed to test a hypothesis regarding the...

Does Physician's Choice of When to Perform EHR Tasks Influence Total EHR Workload?

Does Physician's Choice of When to Perform EHR Tasks Influence Total EHR Workload?

Construção de curso sobre Mini-CEX na modalidade de ensino a distância para preceptores de residência médica

Abstract Introduction: Assessment is paramount in the resident physician’s teaching-learning process. The Mini-CEX is considered an appropriate instrument to carry out formative assessment, allowing structured educational feedback. In this context, residency preceptors need to undergo a continuous training process to better prepare their residents, and a viable form of training is using Distance Education (EaD). Objective: To develop and validate a Distance Education (EaD) course on Mini-CEX for residency preceptors in Mastology. Method: Development and validation of a technical-educational product, in the format of a training course and in the Distance Education modality (EaD). The consensus group technique was used in the product content validation process. Semantic validation was carried out through a validation questionnaire applied to the preceptors involved in the residency program in Mastology. Results: A self-instructional and unmediated course was designed and validated, with a total workload of 02 (two) hours. The course was approved by the target audience, as it had an overall average score of 98% satisfaction and a Cronbach’s alpha equal to 0.8. Conclusions: The course developed and validated on Mini-CEX presents itself as a relevant option for the training of medical residency preceptors in Mastology and can be extended to any medical residency service. It should be noted that the Distance Education (EaD) course favors the adherence of the target audience, with an accessible format to strengthen knowledge on the subject, in addition to encouraging preceptors to also use the educational evaluation.

Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence

ObjectivesDigital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.MethodsAn AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.ResultsBy excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected—26% (25/95) more than DM screening (p < 0.001)—while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).ConclusionAI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.Key Points• Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload.• Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader.• Retrospective study based on paired mammography and digital breast tomosynthesis screening data.

On Convergence of Multidimensional Workload with Dominant Service Duration to a Stable Process

A service system model introduced by I. Kai and M. S. Takku is considered. A limit theorem on the convergence of finite-dimensional distributions of the total workload process with multidimensional resource to the corresponding distributions of multivariate stable process is proved. The situation is considered when the service durations prevails over the values of multidimensional resources.

Energy Aware Load Balancing in Cloudlet Federation

With the rapid increase of compute intensive tasks and workload, the need of the hour is to manage the total workload due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in federated cloudlet environment, load balancing is the main challenge. Load balancing means to divide the total workload between all the member nodes to get the maximum benefits from the federated environment. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible to the mobile devices in the vicinity. Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various member cloudlets. Different tasks consume different amount of energy for their execution that results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat, the more performance degradation, so it becomes very vital to keep the energy consumption as minimum as possible. To address this problem, this paper proposes a novel scheduling strategy that will distribute the total load between all the member nodes in a federated cloudlet environment for load balancing. The proposed methodology not only considers the energy level of the system on which the task has to be executed but also considers the energy required by the tasks in queue. The proposed methodology is tested in a Cloudlet Federation environment and the results show an improved load balancing in terms of energy consumption.

Collaborative Optimization of Yard Crane Deployment and Inbound Truck Arrivals with Vessel-Dependent Time Windows

Due to mega-ships, increasing container throughput, and nonuniform truck arrivals, many container terminals face challenges of unbalanced workloads of yard equipment, shortage of equipment resources in peak hours, and congestion problem. To solve such issues, we propose a mixed-integer bilevel programming model to optimize the vessel-dependent time windows for inbound trucks and yard crane deployment simultaneously. In the proposed bilevel model, the upper level aims to minimize the total truck waiting time at the container terminal gate and yard, while the lower level is formulated to minimize the total workload overflow to next shift in the whole container yard. The optimal yard crane deployment obtained in the lower level will transfer to the upper level problem to determine the waiting time of trucks in the yard and then affect the truck arrivals pattern. To solve the model, a hybrid algorithm—called hybrid genetic algorithm, based on collective decision optimization—is put forward by combining the genetic algorithm and the collective decision optimization algorithm. Numerical experiments are conducted to validate the proposed approach is effective to simultaneously flatten truck arrivals and improve the efficiency of yard cranes. The proposed approach can significantly reduce container terminals’ truck waiting time.

Physical activity and sport-specific training patterns in Swedish sporting and working trial dogs-A questionnaire survey.

To explore physical activity patterns, including conditioning exercise and sport-specific training, and management routines utilized by handlers of Swedish sporting and working dogs participating in agility, obedience, rally obedience and working trial disciplines. Dog handlers provided information on competition-level dogs through an internet-based cross-sectional and descriptive survey on physical activity, sport-specific training and management. Results are reported overall and stratified by participation in specific disciplines. We received 1615 replies to the questionnaire. After data cleaning, 1582 dogs (98%) remained for the analysis. Of these, 430 participated in agility, 790 in obedience, 596 in rally obedience, and 847 dogs had competed in a working trial, i.e., messenger, protection, search or tracking. Number of disciplines performed by each dog varied between one and five. Most common was participation in one (n = 767, 48%) or two (n = 541, 34%) disciplines. Out of the dogs competing in one discipline, 38% (n = 294) were considered to be specialized as they actively trained only that discipline for ≥10 months per year. The vast majority of the dogs (n = 1129, 71%) received more than 1 h of daily physical activity, e.g., walks, and only n = 51 (3%) were never exercised off-leash. Preferred self-selected gait was trot (n = 907, 57%) and gallop (n = 499, 32%). A fifth (n = 319, 20%) never played with other dogs. The majority (n = 1328, 84%) received more than 1 h of vigorous physical conditioning exercise per week. Almost three quarters (n = 1119, 71%) participated in physical conditioning exercise. Two thirds (n = 953, 60%) participated in at least 3 h of sport-specific training per week and only a very small portion (n = 35, 2%) trained their specific discipline less than once per week. Median total work load, i.e., all daily physical activity, vigorous physical conditioning exercise and sport-specific training, was 16.5 h per week. We observe physical activity at moderate to high durations and moderate to vigorous intensities among Swedish sporting and working trial dogs. Most dogs received physical conditioning exercise, but not all dogs were warmed up before training and competition. Our study provides veterinary professionals and dog trainers with valuable insights on the physical exposures and management routines of sporting and working trial dogs.

Medical resource allocation planning by integrating machine learning and optimization models

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.