Estudio transcultural del EPT-C en terapeutas de orientación dinámica post-racionalistas chilenos y argentinos / Transcultural PST-Q study in chilean and argentinean psychodynamic and post-rationalist psychotherapists

Resumen Antecedentes El objetivo de este artículo es comparar el Estilo Personal del Terapeuta (EPT) en psicólogos clínicos de dos enfoques teóricos, cognitivo post-racionalista y psicodinámicos, provenientes de dos países: Chile y Argentina. Además, se analizó el efecto de las variables demográficas y profesionales sobre el EPT. Método Se trabajó con una muestra compuesta por 138 psicoterapeutas, 50% chilenos ( n =69) y 50% argentinos ( n =69). Tanto en la muestra de terapeutas chilenos como argentinos, el 50,7% ( n =35) se identificaron de orientación cognitivo post-racionalista y el 49,3% ( n =34) de orientación psicodinámica. Se utilizó como instrumento el Cuestionario de Estilo Personal del Terapeuta. Resultados Se encontraron puntajes superiores en las Funciones Expresiva e Instruccional en terapeutas de nacionalidad chilena, y en la comparación de psicodinámicos versus terapeutas post-racionalistas, se registraron diferencias en las cinco dimensiones del EPT. Por último, se compararon a los terapeutas de acuerdo con su nacionalidad y enfoque teórico, y se encontraron diferencias en las funciones Expresiva e Instruccional al comparar psicólogos psicodinámicos de Argentina y Chile. Conclusiones Se discuten las implicancias de estos resultados en función de estudios previos.

Abstract Background The purpose of this article is to compare the Personal Style of the Therapist (PST) in clinical psychologists of two different theoretical orientations, post-rationalist cognitive and psychodynamic, from two countries: Chile and Argentina. Additionally, the effects of demographic and professional variables on PST were analyzed. Methods A sample consisting of 138 psychotherapists, 50% Chilean ( n =69) and 50% Argentinean ( n =69) was used. In both Chilean and Argentinean samples, 50.7% ( n =35) identified as having a post-rationalist cognitive theoretical orientation and 49.3% ( n =34) had a psychodynamic orientation. The Personal Style of the Therapist Questionnaire was used as a measure instrument. Results Higher scores were also found in the Expressive and Instructional Functions among Chilean therapists, and in the comparison between the psychodynamic and post-rationalist cognitive orientations, differences were recorded in all five dimensions of PST. Lastly, therapists were compared according to their nationality and theoretical orientation, and differences in the Expressive and Instructional Functions were found when comparing psychodynamic psychologists from Argentina and Chile. Conclusion The implications of these results are discussed based on previous research studies.

A nationwide study of air pollution from particulate matter and daily hospitalizations for respiratory diseases in Italy.

BACKGROUND/AIM: The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Our aim was to assess the short-term effects of PM10 and PM2.5 on respiratory admissions in the whole country of Italy during 2006-2015. METHODS: We estimated daily PM concentrations at the municipality level using satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). Meta-analysis of province-specific estimates obtained by time-series models, adjusting for temperature, humidity and other confounders, was applied to extrapolate national estimates for each outcome. At last, we tested for effect modification by sex, age, period, and urbanization score. Analyses for PM2.5 were restricted to 2013-2015 cause the goodness of fit of exposure estimation. RESULTS: A total of 4,154,887 respiratory admission were registered during 2006-2015, of which 29% for LRTI, 12% for COPD, 6% for URTI, and 3% for asthma. Daily mean PM10 and PM2.5 concentrations over the study period were 23.3 and 17 µg/m3, respectively. For each 10 µg/m3 increases in PM10 and PM2.5 at lag 0-5 days, we found excess risks of total respiratory diseases equal to 1.20% (95% confidence intervals, 0.92, 1.49) and 1.22% (0.76, 1.68), respectively. The effects for the specific diseases were similar, with the strongest ones for asthma and COPD. Higher effects were found in the elderly and in less urbanized areas. CONCLUSIONS: Short-term exposure to PM is harmful for the respiratory system throughout an entire country, especially in elderly patients. Strong effects can be found also in less urbanized areas.

Impact of different exposure models and spatial resolution on the long-term effects of air pollution.

Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 µm, PM10, and nitrogen dioxide, NO2) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO2 and PM10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km2) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO2 and PM10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO2 and PM10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM10, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 µg/m3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions.

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data.

Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 µg/m3 and PM10 between 20 and 35 µg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.

Using a photochemical model to assess the horizontal, vertical and time distribution of PM(2.5) in a complex area: relationships between the regional and local sources and the meteorological conditions.

A photochemical transport model has been implemented to assess the PM(2.5) spatial and temporal distribution in Venice-Mestre. This is a large city of the eastern Po Valley, which is recognized having among the highest levels of many air pollutants in Europe. This study is a first attempt to evaluate PM(2.5) distribution in such a complex ecosystem strongly affected by several different environments (the adjacent Alps, the lagoon and the sea) that create a spatial discontinuity of climate. Model performance was tested with experimental results. Samples have been collected in three sites representative of different emission characteristics. A second simulation was performed with clean boundary conditions to check the influence of the background concentrations on the study domain. Local and regional contributions were found to be strongly dependent on seasonal conditions and on local meteorology. A further analysis was conducted to predict the PM(2.5) distribution with respect to air mass movements. The non-homogeneity of surfaces affects the Planetary Boundary Layer (PBL) behavior. This consequently influences the vertical distribution of PM(2.5) especially during cold seasons and on occasion of particular meteorological events.