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We introduce a new method for the estimation of production technologies in a multi-input multi-output context, based on OneClass Support Vector Machines with piecewise linear transformation mapping. We compare via a finite-sample simulation study the new technique with Data Envelopment Analysis (DEA) to estimate technical efficiency. The criteria adopted for measuring the performance of the estimators are bias and mean squared error. The simulations reveal that the approach based on machine learning seems to provide better results than DEA in our finite-sample scenarios. We also show how to adapt several well-known technical efficiency measures to the introduced estimator. Finally, we compare the new technique with respect to DEA via its application to an empirical database of USA schools from the Programme for International Student Assessment, where we obtain statistically significant differences in the efficiency scores determined through the Slacks-Based Measure.

In this paper, we propose and compare new methodologies for ranking the importance of variables in productive processes via an adaptation of OneClass Support Vector Machines. In particular, we adapt two methodologies inspired by the machine learning literature: one involving the random shuffling of values of a variable and another one using the objective value of the dual formulation of the model. Additionally, we motivate the use of these type of algorithms in the production context and compare their performance via a computational experiment. We observe that the methodology based on shuffling the values of a variable outperforms the methodology based on the dual formulation. We observe that the shuffling-based methodology correctly ranks the variables in 94% of the scenarios with one relevant input and one irrelevant input. Moreover, it correctly ranks each variable in at least 65% of replications of a scenario with three relevant inputs and one irrelevant input.

Healthcare professionals must play an exemplary role in the field of vaccinology. It is convenient that they are trained during their time at university. The objective of this study was to determine the acceptability of the vaccines against COVID-19 in health sciences students in Spanish universities. A cross-sectional study was performed regarding the acceptance of the vaccines against COVID-19 in students in the Health Sciences Degrees in Spanish universities was performed on a sample of students of nursing, medicine, and pharmacy during the spring of 2021, via an online questionnaire with 36 questions designed ad hoc, self-administered, anonymized, and standardized. There were 1222 students participating, of Spanish nationality (97.4%), women (80.5%) and with an average age of 22.0 ± 4.8 years old. Of those, 12.3% had had the disease, 44.0% had to quarantine, 70.8% had undergone diagnostic tests, out of which 14.1% were positive. In total, 97.5% of those surveyed indicated their desire of being vaccinated, if possible, with Comirnaty® (74.9%). At the time of the study, 49.6% were already vaccinated. The reasons for vaccination differed according to the degree and the doubts about vaccine safety was the largest reason for reluctance. Some 37.7% suspected that there are unknown adverse effects and 85.6% of those vaccinated experienced some mild effects after injection. Vaccine acceptance and confidence in the recommendations given by health authorities is high in health sciences students.