Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to predict the potential effects of message delivery from the observation of the psychosocial antecedents. Probabilities were given within the referenced paper. The vertices are:
(high, medium, low);
(high, medium, low);
(high, medium, low);
(high, medium, low);
(high_positive, low_positive, neutral, low_negative, high_negative);
(gain, nonloss, nongain, loss);
(high, medium, low);
(high, medium, low);
(high, medium, low);
(high, medium, low);
(high, medium, low);
Catellani, P., Carfora, V., & Piastra, M. (2022). Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model. Frontiers in Psychology, 13, 825602.