Underlying construct of empathy, optimism, and burnout in medical students.

OBJECTIVE: This study was designed to explore the underlying construct of measures of empathy, optimism, and burnout in medical students. METHODS: Three instruments for measuring empathy (Jefferson Scale of Empathy, JSE); Optimism (the Life Orientation Test-Revised, LOT-R); and burnout (the Maslach Burnout Inventory, MBI, which includes three scales of Emotional Exhaustion, Depersonalization, and Personal Accomplishment) were administered to 265 third-year students at Sidney Kimmel (formerly Jefferson) Medical College at Thomas Jefferson University. Data were subjected to factor analysis to examine relationships among measures of empathy, optimism, and burnout in a multivariate statistical model. RESULTS: Factor analysis (principal component with oblique rotation) resulted in two underlying constructs, each with an eigenvalue greater than one. The first factor involved positive personality attributes (factor coefficients greater than .58 for measures of empathy, optimism, and personal accomplishment). The second factor involved negative personality attributes (factor coefficients greater than .78 for measures of emotional exhaustion, and depersonalization). CONCLUSIONS: Results confirmed that an association exists between empathy in the context of patient care and personality characteristics that are conducive to relationship building, and considered to be positive personality attributes, as opposed to personality characteristics that are considered as negative personality attributes that are detrimental to interpersonal relationships. Implications for the professional development of physicians-in-training and in-practice are discussed

Psychometrics of the scale of attitudes toward physician-pharmacist collaboration: a study with medical students.

BACKGROUND: Despite the emphasis placed on interdisciplinary education and interprofessional collaboration between physicians and pharmacologists, no psychometrically sound instrument is available to measure attitudes toward collaborative relationships. AIM: This study was designed to examine psychometrics of an instrument for measuring attitudes toward physician-pharmacist collaborative relationships for administration to students in medical and pharmacy schools and to physicians and pharmacists. METHODS: The Scale of Attitudes Toward Physician-Pharmacist Collaboration was completed by 210 students at Jefferson Medical College. Factor analysis and correlational methods were used to examine psychometrics of the instrument. RESULTS: Consistent with the conceptual framework of interprofessional collaboration, three underlying constructs, namely responsibility and accountability; shared authority; and interdisciplinary education emerged from the factor analysis of the instrument providing support for its construct validity. The reliability coefficient alpha for the instrument was 0.90. The instrument\u27s criterion-related validity coefficient with scores of a validated instrument (Jefferson Scale of Attitudes Toward Physician-Nurse Collaboration) was 0.70. CONCLUSIONS: Findings provide support for the validity and reliability of the instrument for medical students. The instrument has the potential to be used for the evaluation of interdisciplinary education in medical and pharmacy schools, and for the evaluation of patient outcomes resulting from collaborative physician-pharmacist relationships

Probabilistic inversions of electrical resistivity tomography data with a machine learning-based forward operator

Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modeling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non-linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modeling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: a Markov Chain Monte Carlo algorithm is applied to synthetic data, while an ensemble-based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator three that is orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modeling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources

Machine learning-accelerated gradient-based Markov Chain Monte Carlo inversion applied to electrical resistivity tomography

Expensive forward model evaluations and the curse of dimensionality usually hinder applications of Markov chain Monte Carlo algorithms to geophysical inverse problems. Another challenge of these methods is related to the definition of an appropriate proposal distribution that simultaneously should be inexpensive to manipulate and a good approximation of the posterior density. Here we present a gradient-based Markov chain Monte Carlo inversion algorithm that is applied to cast the electrical resistivity tomography into a probabilistic framework. The sampling is accelerated by exploiting the Hessian and gradient information of the negative log-posterior to define a proposal that is a local, Gaussian approximation of the target posterior probability. On the one hand, the computing time to run the many forward evaluations needed for both the data likelihood evaluation and the Hessian and gradient computation is decreased by training a residual neural network to predict the forward mapping between the resistivity model and the apparent resistivity value. On the other hand, the curse of dimensionality issue and the computational effort related to the Hessian and gradient manipulation are decreased by compressing data and model spaces through a discrete cosine transform. A non-parametric distribution is assumed as the prior probability density function. The method is first demonstrated on synthetic data and then applied to field measurements. The outcomes provided by the presented approach are also benchmarked against those obtained when a computationally expensive finite-element code is employed for forward modelling, with the results of a gradient-free Markov chain Monte Carlo inversion, and also compared with the predictions of a deterministic inversion. The implemented approach not only guarantees uncertainty assessments and model predictions comparable with those achieved by more standard inversion strategies, but also drastically decreases the computational cost of the probabilistic inversion, making it similar to that of a deterministic inversion

The devil is in the third year: a longitudinal study of erosion of empathy in medical school.

PURPOSE: This longitudinal study was designed to examine changes in medical students\u27 empathy during medical school and to determine when the most significant changes occur. METHOD: Four hundred fifty-six students who entered Jefferson Medical College in 2002 (n = 227) and 2004 (n = 229) completed the Jefferson Scale of Physician Empathy at five different times: at entry into medical school on orientation day and subsequently at the end of each academic year. Statistical analyses were performed for the entire cohort, as well as for the matched cohort (participants who identified themselves at all five test administrations) and the unmatched cohort (participants who did not identify themselves in all five test administrations). RESULTS: Statistical analyses showed that empathy scores did not change significantly during the first two years of medical school. However, a significant decline in empathy scores was observed at the end of the third year which persisted until graduation. Findings were similar for the matched cohort (n = 121) and for the rest of the sample (unmatched cohort, n = 335). Patterns of decline in empathy scores were similar for men and women and across specialties. CONCLUSIONS: It is concluded that a significant decline in empathy occurs during the third year of medical school. It is ironic that the erosion of empathy occurs during a time when the curriculum is shifting toward patient-care activities; this is when empathy is most essential. Implications for retaining and enhancing empathy are discussed

Laboratory Tests and Field Surveys to Explore the Optimum Frequency for GPR Surveys in Detecting Qanats

In this paper, we discuss the results of laboratory tests and field surveys using ground penetrating radar (GPR) method to detect qanats at the main campus of Shahid Bahonar University of Kerman (SBUK), Iran. The main purpose of laboratory experiments was to explore the optimum frequency of GPR surveys to detect qanats for the subsoil in the study site. We performed a variety of laboratory tests with a 3 GHz antenna to detect qanats (simulated using dielectric empty targets) hosted by sand with volumetric water content (VWC) values in the range 1.5-8%. The depth to each target was progressively increased until either approaching the edges of the sandbox or modelling a qanat depth for which GPR data could not detect the target anymore. The scaling factors were calculated for each test to estimate the maximum depth of detecting qanats as a function of the scaled GPR frequency. The results showed that in areas where the subsoil is dominated by sand, medium-frequency GPR antennas can penetrate to depths of a few tens of meters, but the penetration depth considerably decreases when the soil moisture and/or clay content of the medium increase. Based on the results of laboratory simulations, qanats are detectable at a maximum normalized depth of about 15-17 times of the wavelengths in very dry sands with VWC less than 5% while the detectable range rapidly drops down to less than 3 or 4 times of the wavelengths in more humid sands with VWC of about 8%. We also discuss the results of a few field GPR surveys that were measured using antennas with the 50 MHz and the 250 MHz frequencies in the northwestern part of the study area. The processed GPR images could detect a qanat in the position compatible with the results of previous remote sensing studies performed in the area. The depth to the detected qanat is 13.5 m, which is a little bit beyond the maximum limit predicted by the laboratory tests

Investigating contiguous master's and non-countiguous master’s degree Courses of Architecture and comparing their adaptability with Architecture Education Factors

Background and Objective:In order to achieve the goals and missions of higher education, experts consider it necessary to conduct numerous research on how to examine and identify the strengths and weaknesses of the curriculum. Evaluation of the content of the training course is done in different ways. Comparing the content of the curriculum with each other is one of the methods used. The content of the course is a set that provides planned opportunities for learners to experience learning through an interactive event. The main purpose of the educational program is to train and prepare learners for life and professional activities in the community. Architecture is a discipline that is a combination of humanities, arts and technical sciences. Consequently, the realization of architecture requires a set of knowledge and wisdom. About 1939, academic education of architecture in associate degree and contiguous master's degree courses, began. However, the bachelor's degree course was founded in 1998. Methods: The present study compared the degree of adaptation of architecture curriculum in a contiguous master's degree courses with that of non-contiguous degrees based on the components of architecture education. This applied study was of descriptive-analytical type and evaluation study in nature. The explanatory method was used for data collection. First, the educational content and whatever an architecture student should learn were discussed. Then, experts' opinions and perceptions were asked regarding the educational content using a likert scale questionnaire. Findings:The findings revealed that the content of architecture teaching is based on the three foundations of knowledge, competence, wisdom, and the course syllabus in contiguous master's degree included 67 units (1767 hours) in knowledge dimension and 88 units (3640 hours) in competence dimension. Overall, both non-contiguous degrees encompassed 64 units (1479 hours) in knowledge dimension and 88 units (3570 hours) in competence. According to professors, employers and graduates’perspectives, contiguous master's degree was more comprehensive in terms of knowledge, competence and wisdom. Considering the nature of the architecture and its difference with other fields of study, as well as the impact of the competence and wisdom factors in architects’ training, and in most areas, prefers a non-countiguous master's degree. Conclusion: In general, in all three areas of knowledge and especially ability and insight, the continuous master's degree is a more successful course. Converting the field of architecture from a bachelor's degree to a continuous master's degree (unlike technical disciplines, etc.) is not a successful experience in Iran, and a bachelor's degree in a continuous master's degree is preferable to a bachelor's degree in most fields.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Peer review and citation data in predicting university rankings, a large-scale analysis

Most Performance-based Research Funding Systems (PRFS) draw on peer review and bibliometric indicators, two different method- ologies which are sometimes combined. A common argument against the use of indicators in such research evaluation exercises is their low corre- lation at the article level with peer review judgments. In this study, we analyse 191,000 papers from 154 higher education institutes which were peer reviewed in a national research evaluation exercise. We combine these data with 6.95 million citations to the original papers. We show that when citation-based indicators are applied at the institutional or departmental level, rather than at the level of individual papers, surpris- ingly large correlations with peer review judgments can be observed, up to r <= 0.802, n = 37, p < 0.001 for some disciplines. In our evaluation of ranking prediction performance based on citation data, we show we can reduce the mean rank prediction error by 25% compared to previous work. This suggests that citation-based indicators are sufficiently aligned with peer review results at the institutional level to be used to lessen the overall burden of peer review on national evaluation exercises leading to considerable cost savings