redmeat {bnRep} | R Documentation |
redmeat Bayesian Network
Description
Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model.
Format
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:
- Baseline_Intention
(high, medium, low);
- Desensitization
(high, medium, low);
- Diffused_Responsibility
(high, medium, low);
- Food_Involvment
(high, medium, low);
- Future_Intention
(high_positive, low_positive, neutral, low_negative, high_negative);
- Message
(gain, nonloss, nongain, loss);
- Perceived_Control
(high, medium, low);
- Perceived_Severity
(high, medium, low);
- Prevention_Focus
(high, medium, low);
- Promotion_Focus
(high, medium, low);
- Systematic_Processing
(high, medium, low);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
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.