Task Time Distribution Research Articles

Utilizing self-report diaries to explore task time distribution of school nurses in UAE.

This study aims to evaluate the roles and tasks of school nurses in the UAE, quantify the time spent on each, and identify areas for improvement in school health services. This aligns with the UAE government's initiative to enhance primary healthcare, focusing on illness prevention and health promotion for children and adolescents. The research adopts an observational study design, utilizing self-observation through diary recordings by school nurses to collect data on their daily tasks and time allocation. A sample of total of 2024 school nurse activities were recorded and analyzed over 126days and 1084h of observation by eight school nurses using self-report diaries. This method allowed for the collection of detailed information on how nursing time is allocated between core and noncore tasks. In this study a total of 2024 tasks were observed over 1084h. The findings reveal that core nursing tasks accounted for 78% of activities but only 53% of the total 1084h observed, while non-nursing tasks, making up 22% of activities, disproportionately consumed 47% of the hours. This discrepancy highlights the inefficiency of time allocation, with non-nursing tasks such as administrative duties taking significantly longer than core patient care tasks. The study highlights a significant opportunity to enhance school health services in the UAE by optimizing the allocation of nursing time towards more illness prevention and health promotion interventions. By addressing the identified challenges, including the gaps in nurse competencies and the lack of structured practice frameworks, school health services can be improved.

Optimal Rate-Matrix Pruning For Heterogeneous Systems

We consider large-scale load balancing systems where processing time distribution of tasks depend on both task and server types. We analyze the system in the asymptotic regime where the number of task and server types tend to infinity proportionally to each other. In such heterogeneous setting, popular policies like Join Fastest Idle Queue (JFIQ), Join Fastest Shortest Queue (JFSQ) are known to perform poorly and they even shrink the stability region. Moreover, to the best of our knowledge, in this setup, finding a scalable policy with provable performance guarantee has been an open question prior to this work. In this paper, we propose and analyze two asymptotically delay-optimal dynamic load balancing approaches: (a) one that efficiently reserves the processing capacity of each server for "good" tasks and route tasks under the Join Idle Queue policy; and (b) a speed-priority policy that increases the probability of servers processing tasks at a high speed. Introducing a novel analytical framework and using the mean-field method and stochastic coupling arguments, we prove that both policies above achieve asymptotic zero queueing, whereby the probability that a typical task is assigned to an idle server tends to 1 as the system scales.

Direct observational time and motion study of the daily activities of hospital dispensary pharmacists and technicians

AbstractBackgroundHospital pharmacy dispensaries are busy work environments where staff are involved in a variety of work‐related tasks. The proportion of time spent on daily tasks, task prioritisation, multitasking, and interruptions remains largely unknown.AimTo examine the tasks performed and proportion of time pharmacists and pharmacy technicians in a hospital pharmacy inpatient dispensary spend on various work‐related activities.MethodPharmacists and technicians working in the inpatient dispensary of a large metropolitan health service were directly observed by trained researchers. Tasks were recorded using Work Observation Method By Activity Timing (WOMBAT), a validated technique developed for direct observation studies of health professionals. Timed tasks were allocated to domains detailing the task performed, who performed it, who they interacted with, and where the task was performed. Data were analysed descriptively with independence of 95% confidence intervals (CI) demonstrating statistical significance.ResultsTwelve pharmacists and 13 technicians were observed for 107.4 h. Tasks that contributed the greatest proportion of time were: the preparation of discharge prescriptions: pharmacists 32.1% (95% CI 29.9–34.3%) and technicians 21.0% (95% CI 18.3–23.7%); inpatient medication supply 22.5% (95% CI 21.5–23.5%) and 49.3% (95% CI 47.3–51.3%) and; inter‐professional communication 13.6% and 14.7% (non‐significant [NS]). Tasks were completed independently 89.6% (pharmacists) and 88.9% (technicians) of the time. Pharmacists and technicians were interrupted 6.7 and 5.1 times per hour (p < 0.05), respectively; 8.6% and 9.5% (NS) of the time was spent undertaking at least two tasks simultaneously.ConclusionThis is the first study to examine task time distribution within a hospital inpatient dispensary. Pharmacists and technicians spend the greatest proportion of time on direct medication dispensing‐related activities. This study demonstrates a high frequency of multitasking and interruptions, both of which are known risks for dispensing errors.

Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration

Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this letter, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents' trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.

Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks.

A lower bound on the stability region of redundancy-[formula omitted] with FIFO service discipline

Due to the complex dynamics of systems with redundancy and cancellations, existing results on the stability region of the Redundancy-d (R(d)) load balancing policy are scarce and are limited to very special cases with respect to the joint service time distribution of tasks. We provide a non-trivial, closed form lower bound on the stability region the R(d) for a general joint service time distribution. While simulation results indicate our bound is not tight, it provides an easy-to-check performance guarantee.

Impact of an electronic health record on task time distribution in a neonatal intensive care unit

BackgroundAn Electronic Health Record (EHR) has been introduced to four Irish maternity units, with further implementations planned. Previous studies indicate that healthcare professionals are concerned that EHRs may increase time spent on documentation and medication-related tasks. ObjectiveTo determine the impact of an EHR on task time distribution in a Neonatal Intensive Care Unit (NICU). MethodsA pre-post, time and motion study. An electronic data collection tool was used to collate time spent on direct care, professional communication, reviewing charts, documentation, and medication-related tasks. Interruptions and contact with the patient zone were quantified. Statistical significance was assessed using two-sample proportion tests, two-sample t-tests, and two-sample Wilcoxon rank-sum tests. A Bonferroni correction set significance at p ≤ 0.0025. Results63 doctors and nurses participated, with 169.23 h of data collected. There were no significant changes to nurses’ task time distribution. The proportion of time spent by doctors on professional communication increased from 15.4% to 26.0% (p < 0.001). Significant increases to median task times were seen for both doctors and nurses. Interruptions to tasks decreased post-implementation (p < 0.001), as did frequency of contact with the patient zone (p < 0.001). ConclusionThe EHR did not redistribute time towards documentation and medication-related tasks. A reduction in interruptions to tasks may streamline workflow. Decreased contact with the patient zone may improve patient safety through reduced potential for pathogen transmission.

Understanding the work of case managers in Australian community aged care: a longitudinal time and motion study.

Objective The aim of this study was to quantify the work activities of community aged care case managers and assess changes following consumer-directed policy reforms. Methods A longitudinal, time and motion study was performed, with direct observation (n=339h) of case managers undertaking work in the office or in the community. We compared the distribution of proportions of time spent across seven broad work task categories during May-August 2014 (P1) and May-October 2016 (P2). Results Office time was primarily consumed by communication (43.7%) and documentation (33.3%) tasks. Documentation increased substantially from P1 to P2 (29.4% vs 37.0% respectively; P<0.001), with more time spent on the subtask of recording information (18.0% vs 24.5% respectively; P=0.039). Travel (45.9%) and communication (41.0%) accounted for most community time. Time in communication increased from P1 to P2 (37.3% vs 48.4% respectively; P=0.047), with more time allocated to client communication (14.6% vs 31.7%; P<0.001). Case managers spent 33.6% of community time in clients' homes (median 25.2min per client; 22.8 vs 30.1min in P1 and P2 respectively) and visited a median of two clients per day (3 vs 1 visits per day in P1 and P2 respectively). Conclusions This study provides the first quantification of task-time distribution among this workforce and how work patterns have changed during a time of significant policy reform and operational changes within the community aged care sector. What is known about the topic? Early qualitative studies gauging case managers' perceptions of the effect of consumer-directed care reforms on their work activities indicate an increase in time spent working directly with aged care clients. However, there is no existing quantitative evidence examining changes to case managers' work activities. What does this paper add? By capturing timed, multidimensional data, this study provides new quantitative evidence of how case managers distribute their time on work activities in office and community settings. Further, the results provide an indication of changes in work task-time distribution over a 2-year period when significant policy reforms and operational changes occurred. Amid a changing aged care landscape, how and with whom case managers spend their time was found to shift, with an increase in time spent recording information and communicating with clients identified. What are the implications for practitioners? This study demonstrates that direct observational studies provide important evidence of the ways in which policy and organisational changes affect community aged care case managers' work activities in practice. Triangulating this quantitative evidence with existing qualitative accounts of policy impact can further allow assessment of how complex reforms may affect everyday work. For policy makers and aged care organisations, such evidence can help discern whether policies and changes are having their desired effects, as well as providing insights as to why or why not.

\u201cHow do we use the time?\u201d \u2013 an observational study measuring the task time distribution of nurses in psychiatric care

BackgroundThe nurse’s primary task in psychiatric care should be to plan for the patient’s care in cooperation with the patient and spend the time needed to build a relationship. Psychiatric care nurses however claim that they lack the necessary time to communicate with patients. To investigate the validity of such claims, this time-motion study aimed at identifying how nurses working at inpatient psychiatric wards distribute their time between a variety of tasks during a working day.MethodsDuring the period of December 2015 and February 2016, a total of 129 h and 23 min of structured observations of 12 nurses were carried out at six inpatient wards at one psychiatric clinic in southern Sweden. Time, frequency of tasks and number of interruptions were recorded and analysed using descriptive statistics.ResultsAdministering drugs or medications accounted for the largest part of the measured time (17.5%) followed by indirect care (16%). Relatively little time was spent on direct care, the third largest category in the study (15.3%), while an unexpectedly high proportion of time (11.3%) was spent on ward related tasks. Nurses were also interrupted in 75% of all medication administering tasks.ConclusionsNurses working in inpatient psychiatric care spend little time in direct contact with the patients and medication administration is interrupted very often. As a result, it is difficult to establish therapeutic relationships with patients. This is an area of concern for both patient safety and nurses’ job satisfaction.

Industrial-Based Time Performance of Assembly Cell Operators: Impact Assessment Characterization by Discrete-Event Simulation

Assembly cells often depend on the human elements when an extended automation is not (economically, even if technologically) possible. The workers’ natural variability is impossible to avoid in a manual assembly system. Usually when simulating an assembly system, a given task time distribution is assumed as the representation of the workers time performance. Workers have variations in their performance that can incur in the shifting of this distribution relative to the expected performance time distribution, as well as in the widening of this distribution, by the increase or decrease of dispersion. This paper presents a discrete event simulation model of an assembly system where the operators have different time distributions, aiming to assess their influence in the overall system performance. Those time distributions were obtained in industrial context, in a previous study, by observing workers in an assembly cell, so representing real performance of workers. The results indicate that the worst performing worker will “pace” the output system performance to a slower rhythm, while better performances of a single worker will only increase very slightly the system productivity.

Modelling reaction time distribution of fast decision tasks in schizophrenia: Evidence for novel candidate endophenotypes

Increased reaction time (RT) and variability of RT in fast decision tasks is observed in patients with schizophrenia and their first degree relatives. This study used modelling of the RT distribution with the aim of identifying novel candidate endophenotypes for schizophrenia. 20 patients with schizophrenia, 15 siblings of patients and 25 healthy controls performed an oddball task of varying working memory load. Increases in mean and standard deviation (SD) of RT were observed for both patients and siblings compared to controls and they were again independent of working memory load. Ex-Gaussian modelling of the RT distribution confirmed that parameters μ, σ and τ increased significantly in patients and siblings compared to controls. The Drift Diffusion Model was applied on RT distributions. A decrease in the diffusion drift rate (v) modeling the accumulation of evidence for reaching the decision to choose one stimulus over the other, was observed in patients and siblings compared to controls. The mean time of the non-decisional sensorimotor processes (t0) and it's variance (st0) was also increased in patients and siblings compared to controls. In conclusion modeling of the RT distribution revealed novel potential cognitive endophenotypes in the quest of heritable risk factors for schizophrenia.

Analysing the impact of a case management model on the specialised palliative care multi-professional team.

Palliative care (PC) involves many health care providers leading to a high complexity of structures that requires efficient coordination as provided by case management (CM). Our study aimed to evaluate the effects of CM newly implemented in a specialised palliative care unit by evaluating team members' tasks and time resources before (T0) and after implementation (T1). It was hypothesised that team members would be able to spend less time on organisational and administrative tasks and more time on patient care. A prospective pre-post study design was used. Time distribution of different tasks at T0 and T1 was compared between three different professional groups: physicians, nursing staff and social work. To document their tasks and time required, a documentation form with several items for each profession was developed. Data was analysed using t tests for independent samples. After Bonferroni correction, a significant pre-post difference was found for the time spent on "discharge interview and discharge" as rated by the physicians (p<.001) with less time spent on this activity after CM implementation. The nursing staff had significantly more time for "conversations with patients" (p<.001) after CM implementation. Moreover, they spent significantly less time on "patient-related requests/advice by telephone" (p<.001) and "discussions with relatives/participation in family discussions" at post-assessment (p<.001). The social worker had significantly more time for "patient advice and support services" (p=.013) after CM implementation. Integrating CM can be time efficient and ensures team members to spend more time on their core tasks in patient care.

Effort and Effectiveness Considerations in Architectural Design: Two Case Studies of Architectural Design Studios

Contemporary architectural design processes show a high degree of complexity: While planners try to fulfill their client’s expectations, they are confronted with a set of constraints and requirements. These include legal constraints such as different building codes, minimum requirements of thermal, acoustical and energy-related indicators as well as structural integrity. In many planning processes, however, the minimum requirements are even less stringent than self-defined expectations. These could - for instance - include a small environmental footprint of the suggested building materials, near zero heating demand, etc. Furthermore, the majority of architectural design processes face a high level of time and cost pressure. Little is known about the time and effort distribution to different processes within such planning processes. These could include consultation with administrative bodies or specific technical planners, performing computational analyses, sketching conceptional options or layouting final designs. In this context, the present contribution explores the time and effort distribution regarding different tasks within the design process in the framework of two design studios. These were conducted with graduate architectural students of the TU Vienna in 2014 and 2015. The concept of these design studios focused on a realistic emulation of real-world project development processes: Participating students were confronted with different (and evolving) visions of their clients, were asked to perform consultation visits with legal bodies of the Viennese building regulation department, and to monitor their time effort for different tasks. These tasks were defined prior to the design studios and allowed for rather detailed classification of different design steps. The two design studios included a roof top extension of a classical 19th century building in Vienna, and the retrofit and extension of a detached house from the 1930ies. This contribution focuses on the time and effort distribution of the different tasks and explores if a general pattern can be identified and derived from the experiences within these design studios.

Comparison of direct and indirect nursing care time in 8-hour and 12-hour shifts

AbstractIntroduction: Nurses are the largest and one of the most important groups of health care providers. The quality of health care mostly depends on the quality of direct and indirect nursing care. Measuring the average nursing care time is an objective measure for assessing the quality of patient care.The goal of the present study was to compare the time being devoted to professional – nonprofessional activities by nurses in 8 hours and 12 hours working shift in Shahid Modarres teaching hospital in Tehran.Methods: A descriptive- comparative study was conducted on 30 nurses (27 female, and 3 male) working in medical and surgical wards of Shahid Modarres teaching hospital in Tehran. The nurses were working 8 and 12 hours shifts and in rotating schedule. Data were collected by direct observation of nurses’ activities,using a checklist with list of direct, indirect and nonprofessional nursing activities. The reliability of the checklist was assured by calculating inter observers’ agreement (The duration of time which nurses spent for each activity was recorded. Data were analyzed using SPSS and Wilcoxon test.Results: The findings of the present study revealed that, nurses working 8 hour shift duration, spent 64.99% of their time on professional and 35.3% on nonprofessional activities (41.21% on direct nursing care, 23.78% on indirect nursing care, 22.53% on personnel affairs and 12.5% on administrative activities). Nurses with 12 hour shift duration, spent 65.59% of their times on professional activities and 33.92% on nonprofessional activities (41.57% on direct healthcare, 24.02% on indirect healthcare, 20.94% on personnel affairs and 12.98% administrative activities).Conclusion: Nurses working in both 8 and 12 hours shift spend most of their times for professional activities (direct and indirect healthcare) and devote less time to nonprofessional activities (administrative and personal affairs), and these activities are independent of their shift durationKeywords: Working shift, Nurse, Direct nursing care, Indirect nursing care, Teaching hospital REFERENCES Admi H Tzischinslcy O Epstein R et al (2008). Shift work in nursing: Is it really a risk factor for Nurses health and patients safety?. Nursing Economics. 26 (4)250-256 Farquharson B (2013). How much time do nurses actually spend on patient care? An analysis of real-time data across medical and surgical wards. 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Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit

ObjectiveTo assess the impact of EHR technology on the work and workflow of ICU physicians and compare time spent by ICU resident and attending physicians on various tasks before and after EHR implementation. DesignEHR technology with electronic order management (CPOE, medication administration and pharmacy system) and physician documentation was implemented in October 2007. MeasurementWe collected a total of 289h of observation pre- and post-EHR implementation. We directly observed the work of residents in three ICUs (adult medical/surgical ICU, pediatric ICU and neonatal ICU) and attending physicians in one ICU (adult medical/surgical ICU). ResultsEHR implementation had an impact on the time distribution of tasks as well as the temporal patterns of tasks. After EHR implementation, both residents and attending physicians spent more of their time on clinical review and documentation (40% and 55% increases, respectively). EHR implementation also affected the frequency of switching between tasks, which increased for residents (from 117 to 154tasks per hour) but decreased for attendings (from 138 to 106tasks per hour), and the temporal flow of tasks, in particular around what tasks occurred before and after clinical review and documentation. No changes in the time spent in conversational tasks or the physical care of the patient were observed. ConclusionsThe use of EHR technology has a major impact on ICU physician work (e.g., increased time spent on clinical review and documentation) and workflow (e.g., clinical review and documentation becoming the focal point of many other tasks). Further studies should evaluate the impact of changes in physician work on the quality of care provided.

ADAPT-POLICY: Task Assignment in Server Farms when the Service Time Distributionof Tasks is Not Known A Priori

Service time distribution of certain computing workloads such as static web content is well known. However, for many other computing workloads (e.g., dynamic web content, scientific workloads) the service time distribution is not well understood and it is not correct to assume that these tasks follow a particular distribution. In this paper, we consider task assignment in server farms when both the service time distribution of tasks and (actual) sizes of tasks are not known a priori. We propose an adaptive task assignment policy, called ADAPT-POLICY, which is based on the concept of multiple static-based task assignment policies. ADAPT-POLICY defines a set of policies for a given system taking into account the specific properties of the system. These policies are selected in such a way that they have different performance characteristics under different workload conditions (i.e., service time distributions, etc.). The objective is to use the task assignment policy with the best performance (i.e., the one with the least expected waiting time) to assign tasks. Which task assignment policy performs the best depends on the traffic conditions that vary over time. ADAPT-POLICY determines the best task assignment using the service time distribution of tasks (and various other traffic properties), which is estimated on-line and then it adaptively changes the task assignment policy to suit the most recent traffic conditions. The experimental results show that ADAPT-POLICY can result in significant performance improvements over both static and dynamic task assignment policies.

How much time do nurses have for patients? A longitudinal study quantifying hospital nurses' patterns of task time distribution and interactions with health professionals.

BackgroundTime nurses spend with patients is associated with improved patient outcomes, reduced errors, and patient and nurse satisfaction. Few studies have measured how nurses distribute their time across tasks. We aimed to quantify how nurses distribute their time across tasks, with patients, in individual tasks, and engagement with other health care providers; and how work patterns changed over a two year period.MethodsProspective observational study of 57 nurses for 191.3 hours (109.8 hours in 2005/2006 and 81.5 in 2008), on two wards in a teaching hospital in Australia. The validated Work Observation Method by Activity Timing (WOMBAT) method was applied. Proportions of time in 10 categories of work, average time per task, time with patients and others, information tools used, and rates of interruptions and multi-tasking were calculated.ResultsNurses spent 37.0%[95%CI: 34.5, 39.3] of their time with patients, which did not change in year 3 [35.7%; 95%CI: 33.3, 38.0]. Direct care, indirect care, medication tasks and professional communication together consumed 76.4% of nurses' time in year 1 and 81.0% in year 3. Time on direct and indirect care increased significantly (respectively 20.4% to 24.8%, P < 0.01;13.0% to 16.1%, P < 0.01). Proportion of time on medication tasks (19.0%) did not change. Time in professional communication declined (24.0% to 19.2%, P < 0.05). Nurses completed an average of 72.3 tasks per hour, with a mean task length of 55 seconds. Interruptions arose at an average rate of two per hour, but medication tasks incurred 27% of all interruptions. In 25% of medication tasks nurses multi-tasked. Between years 1 and 3 nurses spent more time alone, from 27.5%[95%CI 24.5, 30.6] to 39.4%[34.9, 43.9]. Time with health professionals other than nurses was low and did not change.ConclusionsNurses spent around 37% of their time with patients which did not change. Work patterns were increasingly fragmented with rapid changes between tasks of short length. Interruptions were modest but their substantial over-representation among medication tasks raises potential safety concerns. There was no evidence of an increase in team-based, multi-disciplinary care. Over time nurses spent significantly less time talking with colleagues and more time alone.

A model of human behavior dynamics and exact results

Baraba′si has proposed a queue model of the human behavior. In this paper we compare the Baraba′si results and Va′zquez results. We proposed a model of human behavior dynamics by taking into consideration the service-time distribution. We obtained exact results for the model by calculating the waiting time distribution and sojourn time distribution of active tasks. The results show that the waiting time distribution of the model is a power-law distribution with exponent 2 for the highest priority first selection protocol. This exponent approximates to degree exponent 2.1 observed in Hsue-Shen Tsien correspondence pattern.

Problem-based learning versus lectures: Comparison of academic results and time devoted by teachers in a course on Dentistry in Special Patients

Problem-Based Learning (PBL) is a teaching-learning technique centred on the complete development of the student. It has been successfully implemented in several universities, notably in the health sciences. The process of creating the European Higher Education Area, initiated in Bologna, encourages European universities to use new methodologies in the teaching-learning process, including PBL. Our objectives were to analyze the results obtained by using PBL with fifth-year Dentistry students. Comparison of academic results between students receiving lectures and PBL participants, and assessment of differences between them in the time devoted to tasks by students and teachers. PBL participants obtained higher grades compared to those receiving lectures only (p<0.05). The two student groups devoted the same amount of time to this subject but the time distribution of tasks was very different, with PBL students spending more time on group work and analysis of the literature. The teachers devoted much more time to the PBL group. PBL is a teaching-learning methodology that improves student academic results. PBL diverts student time to more complex tasks but requires a greater commitment from the teachers.

Asymptotic Behavior of Total Times for Jobs That Must Start Over if a Failure Occurs

Many processes must complete in the presence of failures. Different systems respond to task failure in different ways. The system may resume a failed task from the failure point (or a saved checkpoint shortly before the failure point), it may give up on the task and select a replacement task from the ready queue, or it may restart the task. The behavior of systems under the first two scenarios is well documented, but the third (RESTART) has resisted detailed analysis. In this paper we derive tight asymptotic relations between the distribution of task times without failures and the total time when including failures, for any failure distribution. In particular, we show that if the task-time distribution has an unbounded support, then the total-time distribution H is always heavy tailed. Asymptotic expressions are given for the tail of H in various scenarios. The key ingredients of the analysis are the Cramér–Lundberg asymptotics for geometric sums and integral asymptotics, which in some cases are obtained via Tauberian theorems and in some cases by barehand calculations.