From the boom to the crisis: changes in employment conditions of immigrants in Spain and their effects on mental health

Background: Migrant workers have been one of the groups most affected by the economic crisis. This study evaluates the influence of changes in employment conditions on the incidence of poor mental health of immigrant workers in Spain, after a period of 3 years, in context of economic crisis. Methods: Follow-up survey was conducted at two time points, 2008 and 2011, with a reference population of 318 workers from Colombia, Ecuador, Morocco and Romania residing in Spain. Individuals from this population who reported good mental health in the 2008 survey (n = 214) were interviewed again in 2011 to evaluate their mental health status and the effects of their different employment situations since 2008 by calculating crude and adjusted odds ratios (aORs) for sociodemographic and employment characteristics. Findings: There was an increased risk of poor mental health in workers who lost their jobs (aOR = 3.62, 95%CI: 1.64–7.96), whose number of working hours increased (aOR = 2.35, 95%CI: 1.02–5.44), whose monthly income decreased (aOR = 2.75, 95%CI: 1.08–7.00) or who remained within the low-income bracket. This was also the case for people whose legal status (permission for working and residing in Spain) was temporary or permanent compared with those with Spanish nationality (aOR = 3.32, 95%CI: 1.15–9.58) or illegal (aOR = 17.34, 95%CI: 1.96–153.23). In contrast, a decreased risk was observed among those who attained their registration under Spanish Social Security system (aOR = 0.10, 95%CI: 0.02–0.48). Conclusion: There was an increase in poor mental health among immigrant workers who experienced deterioration in their employment conditions, probably influenced by the economic crisis.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).