Desigualdades de género en salud laboral en España

Objetivos: Analizar las desigualdades de género en las condiciones de empleo, trabajo, conciliación de la vida laboral y familiar, y en los problemas de salud relacionados con el trabajo en una muestra de la población ocupada en España en el año 2007 teniendo en cuenta la clase social y el sector de actividad. Métodos: Las desigualdades de género se analizaron mediante 25 indicadores en los 11.054 trabajadores entrevistados en la VI Encuesta Nacional de Condiciones de Trabajo. Se calcularon las odds ratio (OR) y sus intervalos de confianza del 95% (IC95%) mediante modelos de regresión logística multivariados, estratificando por clase social ocupacional y sector de actividad. Resultados: Más mujeres que hombres trabajaban sin contrato (OR = 1,83; IC95%: 1,51-2,21), con alto esfuerzo o baja recompensa (1,14:1,05-1,25) y sufriendo acoso sexual (2,85:1,75-4,62), discriminación (1,60:1,26-2,03) y más dolores osteomusculares (1,38:1,19-1,59). Más hombres que mujeres trabajaban a turnos (0,86:0,79-0,94), con altos niveles de ruido (0,34:0,30-0,40), altas exigencias físicas (0,58:0,54-0,63) y sufriendo más lesiones por accidentes de trabajo (0,67:0,59-0,76). Las trabajadoras no manuales mostraron trabajar con un contrato temporal (1,34:1,09-1,63), expuestas a más riesgos psicosociales y sufriendo mayor discriminación (2,47:1,49-4,09) y enfermedades profesionales (1,91:1,28-2,83). En el sector de la industria las desigualdades de género fueron más marcadas. Conclusiones: En España existen importantes desigualdades de género en las condiciones de empleo, trabajo y en los problemas de salud relacionados con el trabajo, que se ven influenciadas por la clase social y el sector de actividad, y que sería necesario tener en consideración en las políticas públicas de salud laboral.

Social determinants of self-perceived discrimination in Spain

Objectives: To analyse the association between self-perceived discrimination and social determinants (social class, gender, country of origin) in Spain, and further to describe contextual factors which contribute to self-perceived discrimination. Methods: Cross-sectional design using data from the Spanish National Health Survey (2006). The dependent variable was self-perceived discrimination, and independent and stratifying variables were sociodemographic characteristics (e.g. sex, social class, country of origin, educational level). Logistic regression was used. Results: The prevalence of self-perceived discrimination was 4.2% for men and 6.3% for women. The likelihood of self-perceived discrimination was higher in people who originated from low-income countries: men, odds ratio (OR) 5.59 [95% confidence interval (CI) 4.55–6.87]; women, OR 4.06 (95% CI 3.42–4.83). Women were more likely to report self-perceived discrimination by their partner at home than men [OR 8.35 (95% CI 4.70–14.84)]. The likelihood of self-perceived discrimination when seeking work was higher among people who originated from low-income countries than their Spanish counterparts: men, OR 13.65 (95% CI 9.62–19.35); women, OR 10.64 (95% CI 8.31–13.62). In comparison with Spaniards, male white-collar workers who originated from low-income countries [OR 11.93 (95% CI 8.26–17.23)] and female blue-collar workers who originated from low-income countries (OR 1.6 (95% CI 1.08–2.39)] reported higher levels of self-perceived discrimination. Conclusions: Self-perceived discrimination is distributed unevenly in Spain and interacts with social inequalities. This particularly affects women and immigrants.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.