Assessing the social impacts of the COVID-19 crisis using phone helplines. The case of the Balearic Islands, Spain.

Background: Crises and health policies to tackle them can increase health inequalities. We explored the scope and usefulness of helplines set up during the COVID-19 crisis and characterised the vulnerability of their users. This study explored the geographic and socioeconomic effects of the telephone helplines set up by the Balearic Islands Government and aimed to characterise the vulnerability of their users. Methods: Telephonic survey combined with a geographical analysis of a sample of calls made between 15th of March and 30th of June of 2020 to five helplines: COVID-19 general information; psychological, social (minimum vital income), labour (temporary employment regulation), and housing (rental assistance) helps. The questionnaire included sociodemographic and housing characteristics, type of problem, and if it was solved or not. We used multinomial regression to explore factors associated with having solved the problem. We calculated the standardised rate of calls by municipality using Chi-squared and z-test to test differences. Results: 1,321 interviews from 2,678 selected (231 excluded, 608 untraceable, and 518 refusals). 63.8% of women, 48.7% were born in another country. They had no internet at home in 3.1%, only on the phone in 17.3%. The 23.5% had no income at home. The Problem was solved in 25.4%, and partly in 30.9%. Factors associated with not solving the problem were not having income at home (p = 0.021), labour (p = 0.008), economic (p = 0.000) or housing (p = 0.000) problems. People from 55 of 67 municipalities did at least one call. The highest rates of calls were from coastal tourist municipalities. Conclusion: Helplines reached most of the territory of the Balearic Islands and were used mainly in tourist municipalities. It probably has not been helpful for families with more significant deprivation. Digital inequalities have emerged.

The Association between Trainer Expertise and Changes in Adolescent Symptomatology in an Evidence-Based Family Prevention Programme.

PURPOSE: The aim of this study is to assess whether the outcomes of implementations of the Universal Strengthening Families Program (SFP 11-14) are linked to the competences or specific skills of the trainers of social field who gave them. METHOD: The analyzed data was based on ratings awarded by the 174 mothers participating in the SFP 11-14. By conducting a K-means cluster analysis, significant groups were identified, based on the ratings awarded to the trainers. RESULTS/DISCUSSION: A comparison of the clusters led to the identification of significant differences between cluster 2 (trainers with limited skills) and the other clusters in terms of changes in the children's symptoms after participating in the SFP 11-14. Trainers with limited skills were associated with fewer changes in the symptomatology. It highlights the importance of proficient trainers in programme outcomes, and it could serve as a guide for the public social work in the design of EBP.

Analysis of professional competencies in the Spanish public administration management / Análisis de las competencias profesionales en las jefaturas de la Administración Pública Española

The present study aims to analyse the degree of importance civil servants in the Spanish Public Administration (SPA) attach to a set of twenty professional competencies, as well as to compare the level of managers’ self-assessed competency and that of a reference population of managers. For this purpose, a sample of 613 public servants in the SPA consisting of lower-ranking officials and managers was chosen and a survey methodology was used for data collection and analysis. The results indicate first that the most relevant competencies for both groups are self-confidence and self-assurance, communication, and teamwork. Secondly, the level of relevance attached by lower-ranking officials is in many cases greater than the level attached by managers. Finally, managers in the SPA show a self-assessed level of competency far below that of the reference population of managers. This set of results provides valuable information for the creation of a competency-based Comprehensive Human Resources Integrated Management System in the SPA (AU)

El presente trabajo tiene como objetivo analizar el grado de importancia que otorgan los funcionarios de la Administración Pública Española (APE) a un conjunto de veinte competencias profesionales así como compararel nivel competencial autoevaluado por sus jefaturas con el de la población de directivos de referencia. Para ello se ha trabajado con una muestra de 613 empleados públicos de la APE compuesta por puestos base y jefaturas y se ha aplicado una metodología de encuestas para la recogida y análisis de datos. Los resultados obtenidos indican, en primer lugar, que las competencias más relevantes para ambos colectivos son la confianza y seguridad en sí mismo, la comunicación y el trabajo en equipo. En segundo lugar, el nivel de relevancia otorgado por los puestos base es, en muchos casos, superior al nivel otorgado por las jefaturas. Finalmente, las jefaturas de la APE muestran un nivel competencial autoevaluado muy por debajo del de la población de directivos de referencia. Este conjunto de resultados aporta información valiosa para la creación de un sistema de gestión integral de los recursos humanos por competencias en la APE (AU)

Using the R-mape index as a resistant measure of forecast accuracy / El índice R-MAPE como medida resistente del ajuste en la previsión

Background: The mean absolute percentage error (MAPE) is probably the most widely used goodness-of-fit measure. However, it does not meet the validity criterion due to the fact that the distribution of the absolute percentage errors is usually skewed to the right, with the presence of outlier values. In these cases, MAPE overstates the corresponding population parameter. In this study, we propose an alternative index, called Resistant MAPE or R-MAPE based on the calculation of the Huber M-estimator, which allows overcoming the aforementioned limitation. Method: The results derived from the application of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models are used to forecast a time series. Results: The arithmetic mean, MAPE, overstates the corresponding population parameter, unlike R-MAPE, on a set of error distributions with a statistically significant right skew, as well as outlier values. Conclusions: Our results suggest that R-MAPE represents a suitable alternative measure of forecast accuracy, due to the fact that it provides a valid assessment of forecast accuracy compared to MAPE (AU)

Antecedentes: el Promedio del Error Porcentual Absoluto (MAPE) es probablemente la medida de adecuación de la previsión más ampliamente utilizada. Sin embargo, no cumple el criterio de validez debido a que la distribución de los errores porcentuales absolutos habitualmente presenta una forma asimétrica a la derecha con presencia de valores alejados. En estos casos, el MAPE proporciona una sobreestimación del correspondiente parámetro poblacional. En el presente trabajo se propone un índice alternativo, denominado MAPE Resistente o R-MAPE, y basado en el cálculo del M-estimador de Huber, el cual permite superar la mencionada limitación. Método: se utilizan los resultados derivados de la aplicación de modelos de Red Neuronal Artificial (ANN) y modelos Autorregresivos Integrados de Media Móvil (ARIMA) en la previsión de una serie temporal. Resultados: se puede observar que la media aritmética, el MAPE, realiza una sobreestimación del correspondiente parámetro poblacional, a diferencia del R-MAPE, sobre un conjunto de distribuciones de errores con asimetría a la derecha y presencia de valores alejados. Conclusiones: nuestros resultados ponen de manifiesto que el R-MAPE representa una adecuada alternativa en la medición del ajuste en la previsión, debido a que proporciona una evaluación válida de dicho ajuste en comparación al MAPE (AU)

Using the R-MAPE index as a resistant measure of forecast accuracy.

BACKGROUND: The mean absolute percentage error (MAPE) is probably the most widely used goodness-of-fit measure. However, it does not meet the validity criterion due to the fact that the distribution of the absolute percentage errors is usually skewed to the right, with the presence of outlier values. In these cases, MAPE overstates the corresponding population parameter. In this study, we propose an alternative index, called Resistant MAPE or R-MAPE based on the calculation of the Huber M-estimator, which allows overcoming the aforementioned limitation. METHOD: The results derived from the application of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models are used to forecast a time series. RESULTS: The arithmetic mean, MAPE, overstates the corresponding population parameter, unlike R-MAPE, on a set of error distributions with a statistically significant right skew, as well as outlier values. CONCLUSIONS: Our results suggest that R-MAPE represents a suitable alternative measure of forecast accuracy, due to the fact that it provides a valid assessment of forecast accuracy compared to MAPE.

Stress associated with hospitalization in patients with COPD: the role of social support and health related quality of life.

BACKGROUND: The objective of this study was to determine stress levels during hospitalization in patients with Chronic Obstructive Pulmonary Disease (COPD). We wanted to relate stress to previous level of quality of life and patients' Social Support. METHODS: 80 patients (70.43; SD = 8.13 years old) with COPD were assessed by means of: Hospital Stress Rating Scale, Nottingham Health Profile, St. George's Respiratory Questionnaire and Social Support Scale. RESULTS: COPD patients' stress levels are lower than expected independently from the severity or number of previous hospitalizations. Linear regression analysis shows the predictive value of Quality of Life and Social Support on stress level during hospitalization (p < 0.0001). CONCLUSION: HRQOL and social support can be associated with stress during hospitalization.

Artificial neural networks applied to forecasting time series / Redes neuronales artificiales aplicadas a la previsión de series temporales

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fi t regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research (AU)

El presente estudio ofrece una descripción y una comparación de los principales modelos de Redes Neuronales Artificiales (RNA) que han demostrado ser de utilidad en la previsión de series temporales, así como un procedimiento estándar para la aplicación práctica de las RNA en este tipo de tareas. Se analizan los modelos Perceptrón Multicapa (MLP), Funciones de Base Radial (RBF), Red Neuronal de Regresión Generalizada (GRNN) y Redes Neuronales Recurrentes (RNN). Para ello, se ha utilizado una serie temporal compuesta por 244 puntos temporales. El estudio comparativo establece que el error cometido por los cuatro modelos de red analizados es inferior al 10%. De acuerdo con los criterios de interpretación de este desempeño, se puede concluir que los modelos de red presentan un alto ajuste en su capacidad de previsión. El modelo con mejor rendimiento es el RBF, seguido del RNN y MLP. El modelo GRNN es el que presenta peor rendimiento. Finalmente, se analizan las ventajas y limitaciones de las RNA, las posibles soluciones a tales limitaciones, así como una orientación de las líneas de investigación futuras (AU)

Las competencias genéricas en la educación superior. Estudio comparativo entre la opinión de empleadores y académicos / Generic skills in higher education. Comparative study of the views of employers and academics

Este trabajo analiza y compara la opinión de los empleadores y de los académicos respecto a la importancia que otorgan a las competencias genéricas en la formación de los titulados y el nivel adquirido en la educación superior. Para ello se ha utilizado una muestra de 500 empresas e instituciones públicas de las Islas Baleares que cuentan con titulados universitarios entre sus trabajadores y una muestra de 173 académicos pertenecientes a la Universitat de les Illes Balears. Se ha utilizado la metodología de encuestas para la recogida de datos y se han comparado los resultados descriptivos obtenidos en los dos grupos analizados. Dichos resultados ponen de manifiesto la divergencia de opinión entre ambos colectivos (AU)

This paper analyzes and compares employers’ and academics’ views on the importance of generic skills in training graduates and the level acquired in higher education. We used a sample of 500 companies and public institutions in the Balearic Islands that have university graduates among their employees and a sample of 173 academics from the University of the Balearic Islands. We used survey methodology to collect the data and compared the descriptive results of these two groups. These results highlight the difference of opinion of the two groups (AU)

The methodology of Data Mining. An application to alcohol consumption in teenagers.

This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), whereby different modelling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030). Although the overall accuracy obtained (% correct predictions) is very similar in the three models analyzed, the Artificial Neural Network (ANN) technique generates the most accurate model (64.1%), followed by Decision Trees (DT) (62.3%) and Naïve Bayes (NB) (59.9%).

La metodología del Data Mining. Una aplicación al consumo de alcohol en adolescentes / The methodology of Data Mining. An application to alcohol consumption in teenagers

El presente trabajo pretende principalmente acercar a los investigadores del campo de las drogodependencias una metodología de análisis de datos orientada al descubrimiento de conocimiento en bases de datos (KDD). El KDD es un proceso que consta de una serie de fases, la más característica de las cuales se denomina Data Mining (DM), en la que se aplican diferentes técnicas de modelado para detectar patrones y relaciones en los datos. Se analizan los factores comunes y diferenciadores de las técnicas DM más ampliamente utilizadas, desde una visión principalmente metodológica, y ejemplificando su uso con datos provenientes del consumo de alcohol en adolescentes y su posible relación con variables de personalidad (N=7030). Aunque la precisión global obtenida (% de predicciones correctas) es muy similar en los tres modelos analizados, las redes neuronales generan el modelo más preciso (64.1%), seguidas de los árboles de decisión (62.3%) y Naive Bayes (59.9%) (AU)

This paper is aimed mainly at making researchers in the field of drug addictions aware of a methodology of data analysis aimed at knowledge discovery in databases (KDD). KDD is a process consisting of a series of phases, the most characteristic of which is called data mining (DM), where by different model ling techniques are applied in order to detect patterns and relationships among the data. Common and differentiating factors between the most widely used DM techniques are analysed, mainly from a methodological viewpoint, and their use is exemplified using data related to alcohol consumption in teenagers and its possible relationship with personality variables (N=7030). Although the overall accuracy obtained (% correct predictions) is very similar in the three models analyzed, the Artificial Neural Network (ANN) technique generates the most accurate model (64.1%), followed by Decision Trees (DT) (62.3%) and Naïve Bayes (NB) (59.9%) (AU)

Redes neuronales artificiales aplicadas al análisis de supervivencia: un estudio comparativo con el modelo de regresión de Cox en su aspecto predictivo / Artificial neural networks applied to the survival analysis: a comparative study with Cox regression model in its predictive aspect

El objetivo de este estudio fue comparar el rendimiento en predicción entre los modelos de Redes Neuronales Artificiales (RNA) y el modelo de riesgos proporcionales de Cox en el contexto del análisis de supervivencia. Más concretamente, se intentó comprobar: a) si el modelo de redes neuronales jerárquicas es más preciso que el modelo de Cox, y b) si el modelo de redes neuronales secuenciales supone una mejora respecto al modelo de redes neuronales jerárquicas. La precisión fue evaluada a partir de medidas de resolución (área bajo la curva ROC) y calibración (prueba de Hosmer-Lemeshow) usando un conjunto de datos de supervivencia. Los resultados mostraron que las redes neuronales jerárquicas tienen un mejor rendimiento en resolución que el modelo de Cox, mientras que las redes secuenciales no suponen una mejora respecto a las redes neuronales jerárquicas. Finalmente, los modelos de RNA proporcionan curvas de supervivencia más ajustadas a la realidad que el modelo de Cox (AU)

The purpose of this study was to compare the performance in prediction between the models of Artificial Neural Networks (ANN) and Cox proportional hazards models in the context of survival analysis. More specifically, we tried to verify: a) if the model of hierarchical neural networks is more accurate than Cox’s model, and b) if the model of sequential neural networks signifies an improvement with respect to the hierarchical neural networks model. The accuracy was evaluated through resolution (the area under the ROC curve) and calibration (Hosmer-Lemeshow test) measures using survival data. Results showed that hierarchical neural networks outperform Cox’s model in resolution while sequential neural networks do not suppose an improvement with respect to hierarchical neural networks. Finally, ANN models produced survival curves that were better adjusted to reality than Cox’s model (AU)