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.

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.

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%).