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Grilo, L. M., Grilo, H. L., Bogas, S. P. & Junça Silva, A. (2017). Multinomial logistic regression in worker’s health. In AIP Conference Proceedings. (pp. 1-6).: American Institute of Physics.

L. Grilo et al.,  "Multinomial logistic regression in worker’s health", in AIP Conf. Proc., American Institute of Physics, 2017, vol. 1906, pp. 1-6

@inproceedings{grilo2017_1732207475014,
	author = "Grilo, L. M. and Grilo, H. L. and Bogas, S. P. and Junça Silva, A.",
	title = "Multinomial logistic regression in worker’s health",
	booktitle = "AIP Conference Proceedings",
	year = "2017",
	editor = "",
	volume = "1906",
	number = "",
	series = "",
	doi = "10.1063/1.5012392",
	pages = "1-6",
	publisher = "American Institute of Physics",
	address = "",
	organization = "",
	url = "https://aip.scitation.org/doi/abs/10.1063/1.5012392"
}

TY  - CPAPER
TI  - Multinomial logistic regression in worker’s health
T2  - AIP Conference Proceedings
VL  - 1906
AU  - Grilo, L. M.
AU  - Grilo, H. L.
AU  - Bogas, S. P.
AU  - Junça Silva, A.
PY  - 2017
SP  - 1-6
SN  - 1551-7616
DO  - 10.1063/1.5012392
UR  - https://aip.scitation.org/doi/abs/10.1063/1.5012392
AB  - In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services’ organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
ER  -