The American Faculty Schuster, Jack H; Finkelstein, Martin J
Johns Hopkins University Press,
2006, 2008-00-00, 2008-12-15
eBook, Book
Contents: PART I. Overview of the American Faculty (1. Establishing the Framework. - 2. The American Faculty in Perspective. - 3. The Professoriate in Profile). - PART II. The Faculty at Work (4. The ...Changing Complexion of Faculty Work. - 5. Academic Culture and Values and the Quality of Work Life). - PART III. The Academic Career (6. The Changing Academic Career. - 7. The Revolution in Academic Appointments - a closer look. - 8. Compensation and Academic Careers - trends and issues. - 9. Pathways to the Professoriate). - PART IV. Assessing contemporary academic life (10. American Academic life restructured. - 11. What's ahead? Agendas for Policy Analysis, Research, and Action on Academic Staffing (HoF/text adopted).
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable ...for extending a pre-trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this paper proposes a concatenated structure of deep recurrent and 3D convolutional neural networks (R3DCNNs) to learn EEG features across different tasks without prior knowledge. First, this paper adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3DCNN is proposed to simultaneously learn EEG features from the spatial, spectral, and temporal dimensions. The proposed model is validated based on the EEG signals collected from 20 subjects. This paper employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3DCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3DCNN is capable of identifying the mental workload levels for cross-task conditions.
Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on ...cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three important contributions: (1) The performance of ensembles of individually-trained models is statistically indistinguishable from group-trained methods at most sequence lengths. These ensembles can be trained for a fraction of the computational cost compared to group-trained methods and enable simpler model updates. (2) While increasing temporal sequence length improves mean accuracy, it is not sufficient to overcome distributional dissimilarities between individuals' EEG data, as it results in statistically significant increases in cross-participant variance. (3) Compared to all other networks evaluated, a novel convolutional-recurrent model using multi-path subnetworks and bi-directional, residual recurrent layers resulted in statistically significant increases in predictive accuracy and decreases in cross-participant variance.
Introduction
The need to measure reporting workload in teaching departments remains a current pertinent need. In Australia, the Pitman–Jones reporting RVUs have been in the public domain since 2003 ...(revised in 2009). These are expressed in arbitrary units. In New Zealand, single‐site Christchurch Hospital reporting times have been validated and placed in the public domain. Concurrently, the RANZCR has been developing a formulaic descriptor lexicon for imaging studies (the Body System Framework). There is a need to bring these three strands of work together into a common public resource. In 2015, under the auspices of the Chief Accreditation Officer, RANZCR convened the Radiologist Workload Working Group. The goal of the group was to develop a robust method of measuring radiologist workload in teaching departments in Australia and New Zealand for the RANZCR accreditation processes of teaching departments as training sites. This paper concerns itself with one aspect of the group's work, namely reaching a consensus on radiologist study ascribable times for common imaging studies.
Method
The BSF examination descriptors were reduced to a smaller, generic dataset of descriptors at the expense of loss of specificity. BSF study ascribable times had been previously obtained by stopwatch observation. The dataset was harmonized with the Christchurch descriptors to ensure commonality of case mix. The two lead authors reached an approximate consensus study ascribable time for each descriptor in agreement with the BSF and Christchurch data. Specifically, the Christchurch reporting times were relied on extensively to validate the new dataset's study ascribable times. The first draft of descriptors and times was tabled at the meetings of the RANZCR Radiologist Workload Working Group, and was progressively refined by iterative consensus.
Results
The output of the Radiologist Workload Working Group comprises a simplified modality‐based table of robust descriptors and ‘best estimate’ corresponding study ascribable times. These can be used with the extant Pitman–Jones methodology in order to estimate the reporting workload of a medical imaging teaching department in units of time. As a first for Australia and New Zealand, nuclear medicine and PET study ascribable times have been incorporated and balanced against radiology study ascribable times.
Conclusion
The RANZCR 2016 study ascribable times are ready for use by the Australian and New Zealand radiologist and nuclear medicine specialist community. We hope these times will also stimulate further data collection in our two countries towards a robust, bi‐national study ascribable times database.
This paper investigates the amount of academic service performed by female versus male faculty. We use 2014 data from a large national survey of faculty at more than 140 institutions as well as 2012 ...data from an online annual performance reporting system for tenured and tenure–track faculty at two campuses of a large public, Midwestern University. We find evidence in both data sources that, on average, women faculty perform significantly more service than men, controlling for rank, race/ethnicity, and field or department. Our analyses suggest that the male–female differential is driven more by internal service—i.e., service to the university, campus, or department—than external service—i.e., service to the local, national, and international communities—although significant heterogeneity exists across field and discipline in the way gender differentials play out.
Increased cognitive workload (CWL) is a well-established entity that can impair surgical performance and increase the likelihood of surgical error. The use of pupil and gaze tracking data is ...increasingly being used to measure CWL objectively in surgery. The aim of this review is to summarize and synthesize the existing evidence that surrounds this.
A systematic review was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search of OVID MEDLINE, IEEE Xplore, Web of Science, Google Scholar, APA PsychINFO, and EMBASE was conducted for articles published in English between 1990 and January 2021. In total, 6791 articles were screened and 32 full-text articles were selected based on the inclusion criteria. A narrative analysis was undertaken in view of the heterogeneity of studies.
Seventy-eight percent of selected studies were deemed high quality. The most frequent surgical environment and task studied was surgical simulation (75%) and performance of laparoscopic skills (56%) respectively. The results demonstrated that the current literature can be broadly categorized into pupil, blink, and gaze metrics used in the assessment of CWL. These can be further categorized according to their use in the context of CWL: (1) direct measurement of CWL (n = 16), (2) determination of expertise level (n = 14), and (3) predictors of performance (n = 2).
Eye-tracking data provide a wealth of information; however, there is marked study heterogeneity. Pupil diameter and gaze entropy demonstrate promise in CWL assessment. Future work will entail the use of artificial intelligence in the form of deep learning and the use of a multisensor platform to accurately measure CWL.
Interruption at work by social media (SM) is a pervasive phenomenon. This study investigated the impact of SM interruptions and task cognitive levels on mental workload (MWL) and physiological ...indexes. Each subject performed six simulated computer tasks differentiated by two factors: task cognitive level and performing condition. MWL was reflected through three categories of data: perceived mental workload, physiological indexes, and primary task performance. The results revealed significant effects of SM interruptions on heart rate, low-frequency/high-frequency (LF/HF) ratio, and skin conductance. ANOVA results showed there were main effects of task cognitive level on LF/HF and skin conductance. These effects during interrupted tasks were more profound. In addition, participants experienced higher MWL and recorded lower primary task performance in the knowledge-based task than the rule- and skill-based tasks. Our findings can guide managers and employees regarding appropriate use of SM in the workplace and better managing interruption and workload.
Practitioner Summary: Office workers suffer from increased overall mental workload due to unpredictable interruptions while working. This study shows that participants' mental workload increased when receiving SM interruptions, which was more profound during complex tasks. This highlights the importance of SM interruptions management for employees' health, performance, and mobile application developers.
Abbreviations: ANOVA: analysis of variance; DSSQ: dundee stress state questionnaire; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalographic; HPA: hypothalamus-pituitaryadrenocortical; HR: heart rate; HRV: heart rate variability; LF/HF: low frequency/high frequency; MSDs: musculoskeletal disorders; MWL: mental workload; NN: normal to normal; RMS: root means square; RR: time duration between two successive R peaks; RT: response time; SC: skin conductance; SDNN: standard deviation of normal to normal; SM: social media; TCL: task cognitive level; TPC: task performing condition; WMC: working memory capacity
In part 2 of this clinical commentary, we highlight the conceptual and methodologic pitfalls evident in current training-load-injury research. These limitations make these studies unsuitable for ...determining how to use new metrics such as acute workload, chronic workload, and their ratio for reducing injury risk. The main overarching concerns are the lack of a conceptual framework and reference models that do not allow for appropriate interpretation of the results to define a causal structure. The lack of any conceptual framework also gives investigators too many degrees of freedom, which can dramatically increase the risk of false discoveries and confirmation bias by forcing the interpretation of results toward common beliefs and accepted training principles. Specifically, we underline methodologic concerns relating to (1) measure of exposures, (2) pitfalls of using ratios, (3) training-load measures, (4) time windows, (5) discretization and reference category, (6) injury definitions, (7) unclear analyses, (8) sample size and generalizability, (9) missing data, and (10) standards and quality of reporting. Given the pitfalls of previous studies, we need to return to our practices before this research influx began, when practitioners relied on traditional training principles (eg, overload progression) and adjusted training loads based on athletes' responses. Training-load measures cannot tell us whether the variations are increasing or decreasing the injury risk; we recommend that practitioners still rely on their expert knowledge and experience.
Faculty workload is difficult to delineate and quantify equitably because of the various factors and diverse roles that define faculty positions. This is especially true in health professions ...education, including pharmacy. Nonetheless, ensuring fair and transparent distribution of faculty workload is necessary for equity and engagement of the faculty workforce. While it is impossible to develop a uniform policy for all faculty, there can be a guide for how workload is developed and measured, especially for promotion or awarding of tenure, focusing on equity and transparency. Developing clear definitions of workload, setting mutually agreed expectations, and sharing transparent workload assignments and distribution within the institution may be needed. It is imperative to discuss an optimal policy for equitable and transparent workload in each institution and in academic pharmacy as a whole; a lack of this effort can create undue hardship for faculty, decrease productivity, potentially worsen faculty morale, and ultimately impair faculty retention.
The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in ...the number of patients per nurse they had to take care of.
The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups.
We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data.
Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care.
With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse.
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