Empathy in senior year and first year medical students: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The importance of fostering the development of empathy in undergraduate students is continuously emphasized in international recommendations for medical education. Paradoxically, some studies in the North-American context using self-reported measures have found that empathy declines during undergraduate medical training. Empathy is also known to be gender dependent- (highest for female medical students) and related to specialty preference - (higher in patient-oriented than technology-oriented specialties). This factor has not been studied in Portuguese medical schools.</p> <p>Methods</p> <p>This is a cross-sectional study of undergraduate medical students on self-rated measures of empathy collected at entrance and at the conclusion of the medical degree, and on the association of empathy measures with gender and specialty preferences in one medical school in Portugal. Empathy was assessed using the Portuguese adaptation of the Jefferson Scale of Physician Empathy-students version (JSPE-spv) among three cohorts of undergraduate medical students in the first (N = 356) and last (N = 120) year. The construct validity of JSPE-spv was cross-validated with Principal Component Analysis and Confirmatory Factor Analysis. Reliability was assessed using Cronbach' Alpha. Global JSPE-spv score differences were examined by year of medical school, gender and specialty preferences (people-oriented vs technology-oriented specialties).</p> <p>Results</p> <p>The empathy scores of students in the final year were higher as compared to first year students (F (1,387) = 19.33, p < .001, ɳ²_p= 0.48; π = 0.99). Female students had higher empathy scores than male students (F (1,387) = 8.82, p < .01, ɳ ²_p= 0.23; π = 0.84). Significant differences in empathy were not found between the students who prefer people-oriented specialties compared to those who favor the technology-oriented specialties (F (1,387) = 2.44, p = .12, ɳ ²_p= 0.06; π = 0.06).</p> <p>Conclusions</p> <p>This cross-sectional study in one medical school in Portugal showed that the empathy measures of senior year students were higher than the scores of freshmen. A longitudinal cohort study is needed to test variations in students' empathy measures throughout medical school.</p

The relationship between spiritual intelligence and burnout in nurses at Jahrom University of Medical Sciences-2018

Introduction: Job burnout is a psychiatric syndrome that causes symptoms such as emotional exhaustion, loss of job satisfaction and dispossess of personality. Spiritual intelligence also includes a type of adaptation and problem-solving behavior that includes the highest levels of growth in different cognitive, ethical, emotional, and interpersonal areas, in order to coordinate the person with the surrounding phenomena and achieving the internal and external integrity. The aim of this study was to determine the relationship between job burnout and spiritual intelligence of nurses.Methods: This is a cross-sectional study that was conducted in 2017 in educational hospitals of Jahrom University of Medical Sciences. 100 nurses were selected randomly among all nurses who had at least 24 months of clinical work experience, had a bachelor's degree and with no known physical and psychological illness. The main instrument for collecting data was two questionnaires; which were completed by nurses in anonymous form after receiving written consent. Maslach Burnout Questionnaire (1985) has been used to measure job burnout. The questionnaire is a 22-item Likert questionnaire that has been compiled from two different dimensions from never to everyday and its severity is from very little to very much; it includes three components of emotional exhaustion, depersonalization, and inadequacy of a person. The formal and content validity of the instruments was examined by 5 university professors and its reliability was calculated by using Cronbach's alpha with the help of 10 nurses for emotional exhaustion, depersonalization and personal adequacy (0.94 _ 0.93_ 0.91), respectively. Spiritual intelligence questionnaire Abdollahzadeh was used to investigate spiritual intelligence. The formal and content validity was obtained with the help of five professors and its reliability was obtained 0.89 by using the Ray test. Data was analyzed by using SPSS19 software.Results: By using linear regression, it was found that the whole average of spiritual intelligence could predict exhaustion (Sig: 0/03 and F :/080), depersonalization (Sig:0/03 F:.891), and individual adequacy (Sig: 0/013 and F : 2.307). Spiritual intelligence subgroups were able to predict emotional exhaustion (Sig:0/045 and 1.05 F :), depersonalization (Sig:0/0304 and F :1.614), and individual adequacy (Sig:0/02 and F1.545).The average score of total spiritual intelligence can predict the emotional exhaustion, depersonalization, and individual adequacy variations. Also, sub-groups of spiritual intelligence could predict variations in emotional exhaustion, depersonalization, and individual adequacy.Conclusion: Spiritual intelligence has indirect relationship with job burnout so that people with the high spiritual intelligence have better performance and lower job burnout.Keywords: Nurses- Spiritual Intelligence - burnou

Relationships between scores on the Jefferson Scale of physician empathy, patient perceptions of physician empathy, and humanistic approaches to patient care: a validity study.

BACKGROUND: Empathy is the backbone of a positive physician-patient relationship. Physician empathy and the patient\u27s awareness of the physician\u27s empathic concern can lead to a more positive clinical outcome. MATERIAL/METHODS: The Jefferson Scale of Physician Empathy (JSPE) was completed by 36 physicians in the Family Medicine residency program at Thomas Jefferson University Hospital, and 90 patients evaluated these physicians by completing the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE), and a survey about physicians\u27 humanistic approaches to patient care. RESULTS: A statistically significant correlation was found between scores of the JSPE and JSPPPE (r=0.48, p CONCLUSIONS: These findings provide further support for the validity of the JSPE. Implications for the assessments of empathy in the physician-patient relationship as related to clinical outcomes are discussed

Communication skills training and the conceptual structure of empathy among medical students

Introduction: Medical and healthcare professionals’ empathy for patients is crucially important for patient care. Some studies have suggested that a significant decline in empathy occurs during clinical training years in medical school as documented by self-assessed empathy scales. Moreover, a recent study provided qualitative evidence that communication skills training in an examination context, such as in an objective structured clinical examination, might stimulate perspective taking but inhibit the development of compassionate care. Therefore, the current study examined how perspective taking and compassionate care relate to medical students’ willingness to show empathic behaviour and how these relations may change with communication skills training. Methods: A total of 295 fourth-year Japanese medical students from three universities completed the Jefferson Empathy Scale and a newly developed set of items on willingness to show empathic behaviour twice after communication skills training, pertaining to post-training and retrospectively for pre-training. Results: The findings indicate that students’ willingness to show empathic behaviour is much more correlated with perspective taking than with compassionate care. Qualitative descriptive analysis of open-ended question responses revealed a difficulty of feeling compassion despite showing empathic behaviour. Discussion: These findings shed light on the conceptual structure of empathy among medical students and generate a number of hypotheses for future intervention and longitudinal studies on the relation between communication skills training and empathy

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

Psychostimulant Drug Abuse and Personality Factors in Medical Students

Purpose: This study was designed to examine the prevalence of psychostimulant drug abuse among medical students and to test the hypothesis that medical students who use psychostimulant drugs for non-medical reasons are characterized by a sensation seeking and aggressive-hostility personality and exhibit lower empathy

Psychometric properties of the Spanish version of the Jefferson Scale of Empathy: making sense of the total score through a second order confirmatory factor analysis

Background: Empathy is a key aspect of the physician-patient interactions. The Jefferson Scale of Empathy (JSE) is one of the most used empathy measures of medical students. The development of cross-cultural empathy studies depends on valid and reliable translations of the JSE. This study sought to: (1) adapt and assess the psychometric properties in Spanish students of the Spanish JSE validated in Mexican students; (2) test a second order latent factor model. Methods: The Spanish JSE was adapted from the Spanish JSE-S, resulting in a final version of the measure. A non-probabilistic sample of 1104 medical students of two Spanish medical schools completed a socio-demographic and the Spanish JSE-S. Descriptive statistics, along with a confirmatory factor analysis, the average variance extracted (AVE), Cronbach's alphas and composite reliability (CR) coefficients were computed. An independent samples t-test was performed to access sex differences. Results: The Spanish JSE-S demonstrated acceptable to good sensitivity (individual items - except for item 2 - and JSE-S total score: -2.72 < Sk < 0.35 and -0.77 < Ku < 7.85), convergent validity (AVE: between 0.28 and 0.45) and reliability (Cronbach's alphas: between 0.62 and 0.78; CR: between 0.62 and 0.87). The confirmatory factor analysis supported the three-factor solution and the second order latent factor model. Conclusions: The findings provide support for the sensitivity, construct validity and reliability of the adapted Spanish JSE-S with Spanish medical students. Data confirm the hypothesized second order latent factor model. This version may be useful in future research examining empathy in Spanish medical students, as well as in cross-cultural studies.info:eu-repo/semantics/publishedVersio

Associations between medical student empathy and personality: A Multi-institutional study

Background: More empathetic physicians are more likely to achieve higher patient satisfaction, adherence to treatments, and health outcomes. In the context of medical education, it is thus important to understand how personality might condition the empathetic development of medical students. Single institutional evidence shows associations between students' personality and empathy. This multi-institutional study aimed to assess such associations across institutions, looking for personality differences between students with high empathy and low empathy levels. Methods: Participants were 472 students from three medical schools in Portugal. They completed validated adaptations to Portuguese of self-report measures of the NEO-Five Factor Inventory(NEO-FFI) and the Jefferson Scale of Physician Empathy(JSPE-spv). Students were categorized into two groups: "Bottom" (low empathy, N = 165) and "Top" (high empathy, N = 169) according to their empathy JSPE-spv total score terciles. Correlation analysis, binary logistic regression analysis and ROC curve analysis were conducted. Results: A regression model with gender, age and university had a predictive power (pseudo R2) for belonging to the top or bottom group of 6.4%. The addition of personality dimensions improved the predictive power to 16.8%. Openness to experience and Agreeableness were important to predict top or bottom empathy scores when gender, age and university were considered." Based on the considered predictors the model correctly classified 69.3% of all students. Conclusions: The present multi-institutional cross-sectional study in Portugal revealed across-school associations between the Big5 dimensions Agreeableness and Openness to experience and the empathy of medical students and that personality made a significant contribution to identify the more empathic students. Therefore, medical schools may need to pay attention to the personality of medical students to understand how to enhance the empathy of medical students