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

[Cibele Russo]

> 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).
>
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