840 resultados para Economic Status


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Some decades of research on emotional development have underlined the contribution of several domains to emotional understanding in childhood. Based on this research, Pons and colleagues (Pons & Harris, 2002; Pons, Harris & Rosnay, 2004) have proposed the Test of Emotion Comprehension (TEC) which assesses nine domains of emotional understanding, namely the recognition of emotions, based on facial expressions; the comprehension of external emotional causes; impact of desire on emotions; emotions based on beliefs; memory influence on emotions; possibility of emotional regulation; possibility of hiding an emotional state; having mixed emotions; contribution of morality to emotional experiences. This instrument was administered individually to 182 Portuguese children aged between 8 and 11 years, of 3rd and 4th grades, in public schools. Additionally, we used the Socially in Action-Peers (SAp) (Rocha, Candeias & Lopes da Silva, 2012) to assess TEC’s criterion-related validity. Mean differences results in TEC by gender and by socio-economic status (SES) were analyzed. The results of the TEC’s psychometric analysis were performed in terms of items’ sensitivity and reliability (stability, test-retest). Finally, in order to explore the theoretical structure underlying TEC a Confirmatory Factor Analysis and a Similarity Structure Analysis were computed. Implications of these findings for emotional understanding assessment and intervention in childhood are discussed.

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La tesi intende contribuire a livello teorico ed empirico al dibattito in tema di segregazione residenziale su base etnica. Negli ultimi anni, infatti, si stanno sviluppando studi e ricerche che mirano a (ri)definire la categoria di segregazione residenziale e le forme che essa può assumere a livello urbano (micro-segregazione, interstizio, segregazione verticale) proprio a partire dall’analisi delle caratteristiche del contesto Mediterraneo o dell’Europa del Sud. In questo quadro, il disegno di ricerca si articola a partire dal caso studio di Bologna e analizza la distribuzione residenziale della popolazione residente straniera in prospettiva diacronica, utilizzando strumenti di analisi georeferenziata e considerando diverse variabili (nazionalità, genere, status-socioeconomico). L’analisi quantitativa viene integrata da una seconda parte, che si compone di 20 interviste biografiche di tipo recall, che ricostruisce le traiettorie abitative di migranti residenti nella Città Metropolitana di Bologna, al fine di comprendere le modalità in cui le dinamiche strutturali che investono l’housing system si concretizzino nelle storie di vita di persone migranti. Dall’analisi emerge che la popolazione residente straniera è investita da un processo di periferizzazione che si manifesta a livello spaziale-territoriale e nei percorsi di vita. Adottandone una definizione estensiva, la categoria di periferizzazione individua una specifica forma di segregazione residenziale che non denota solo un processo di mobilità territoriale ma anche un più ampio e articolato “modo di abitare” di chi sta ai margini.

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Background The aim of this study was to study ecological correlations between age-adjusted all-cause mortality rates in Australian statistical divisions and (1) the proportion of residents that self-identify as Indigenous, (2) remoteness, and (3) socio-economic deprivation. Methods All-cause mortality rates for 57 statistical divisions were calculated and directly standardized to the 1997 Australian population in 5-year age groups using Australian Bureau of Statistics (ABS) data. The proportion of residents who self-identified as Indigenous was obtained from the 1996 Census. Remoteness was measured using ARIA (Accessibility and Remoteness Index for Australia) values. Socioeconomic deprivation was measured using SEIFA (Socio-Economic index for Australia) values from the ABS. Results Age-standardized all-cause mortality varies twofold from 5.7 to 11.3 per 1000 across Australian statistical divisions. Strongest correlation was between Indigenous status and mortality (r = 0.69, p < 0.001). correlation between remoteness and mortality was modest (r = 0.39, p = 0.002) as was correlation between socio-economic deprivation and mortality (r = -0.42, p = 0.001). Excluding the three divisions with the highest mortality, a multiple regression model using the logarithm of the adjusted mortality rate as the dependent variable showed that the partial correlation (and hence proportion of the variance explained) for Indigenous status was 0.03 (9 per cent; p = 0.03), for SEIFA score was -0.17 (3 per cent; p = 0.22); and for remoteness was -0.22 (5 per cent; p = 0.13). Collectively, the three variables studied explain 13 per cent of the variability in mortality. Conclusions Ecological correlation exists between all-cause mortality, Indigenous status, remoteness and disadvantage across Australia. The strongest correlation is with indigenous status, and correlation with all three characteristics is weak when the three statistical divisions with the highest mortality rates are excluded. intervention targeted at these three statistical divisions could reduce much of the variability in mortality in Australia.