[ABE-L] Seminário Conjunto PIPGEs - ICMC/UFSCar - 15/12/2014 - 14h00 - Emmanuel Lesaffre

[Dalton Andrade]

Cibele,
 Gostaria de ter o material do seminário. Poderias disponibilizá-lo?
 Obrigado,

Dalton

Enviada do meu iPhone

> Em 03/12/2014, às 11:49, Cibele Russo <cibele em icmc.usp.br> escreveu:
> 
> Divulgando:
> 
> 
> Seminário Conjunto PIPGEs - ICMC/UFSCar - 15/12/2014 - 14h00
> 
> LOCAL: Auditório Luiz Antonio Favaro (sala 4-111) – ICMC - USP, São Carlos, SP.
> 
> TÍTULO: Exploring burnout in a large European nurse survey using a multilevel covariance regression model
> 
> Palestrante: Emmanuel Lesaffre (Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands, L-Biostat, KULeuven, Leuven, Belgium)
> 
> Resumo:  We propose a novel modeling approach that can model both the mean structure and the covariance structure with a mixed effects model in a multivariate context. We called this the multilevel covariance regression (MCR) model. When the dimension of the response is high, a joint model of a multilevel factor analytic (MFA) model and an MCR model (MHOF model) is then proposed. 
> We applied the MCR model to data from the RN4CAST (Sermeus et al. 2011) FP7 project which involves 33,731 registered nurses in 2,169 nursing units in 486 hospitals in 12 European countries. The MHOF model was applied to the Belgium part of the project. As response we have taken in the first analysis the historically derived three burnout dimensions (Maslach and Jackson, 1981), while the MHOF model is based on the raw data, i.e. the responses to the 22-item questionnaire. The three burnout dimensions are emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). Applying the MHOF model to burnout could address the following questions simultaneously: 1) is the burnout structure the same as the commonly used structure by Maslach and Jackson? 2) how much variation of burnout could be explained by the level-specific fixed and random effects? 3) do the variances and correlations among burnout stay constant across level-specific characteristics and units at each level? 
> We opted for the Bayesian approach as our estimating method for the MCR and MHOF models. The JAGS (just another Gibbs sampler) MCMC (Markov chain Monte Carlo) program was used through the R package rjags. Most parameters were assigned a non-informative prior except for the fixed and random effects in the factor loadings in the MCR part. These parameters were assigned a mixture prior respectively to overcome the "flipping states" issue in Bayesian context. Model comparison was done using the pseudo Bayes factor (PSBF).
> _______________________________________________
> abe mailing list
> abe em lists.ime.usp.br
> https://lists.ime.usp.br/mailman/listinfo/abe
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <https://lists.ime.usp.br/archives/abe/attachments/20141203/3dfe21c4/attachment.html>