Research on the Influence Mechanism and Moderating Effect of Food Safety Awareness on Health Behaviors Based on the Logistic Model
DOI:
https://doi.org/10.62051/ijafsr.v3n1.05Keywords:
Food Safety Awareness, Healthy Behaviors, Logistic Model, Moderating EffectAbstract
Against the backdrop of the steady advancement of the Rural Revitalization Strategy, the health level of rural residents has become a key indicator for measuring development achievements. This study uses descriptive statistical analysis and the Logistic regression model to explore the influence mechanism and moderating effect of food safety awareness on healthy behaviors. The results show that although rural residents have a relatively high level of concern about food safety, their confidence in the food in the market is low. In terms of the dietary structure, the proportion of staple food expenditure has decreased from 45% to 38%, the intake of animal protein is 20% lower than that of urban residents, the consumption of deeply processed foods has increased by 12%, and the overweight rate of BMI reaches 32%, which is higher than the national rural average. Logistic regression analysis shows that the odds ratio of food safety awareness is 1.05, and the significance P - value is less than 0.01. The diversity of information acquisition channels and the coverage rate of policy publicity have significant moderating effects. When the coverage rate of policy publicity increases from 50% to 80%, the probability of healthy behaviors increases by 0.56%. This study provides important data support and strategic references for promoting the healthy diet of rural residents and driving rural revitalization.
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[1] Zhao X, Deng S, Zhou Y. The impact of reference effects on online purchase intention of agricultural products: The moderating role of consumers’ food safety consciousness [J]. Internet Research, 2017, 27(2): 233-255.
[2] Chai D, Meng T, Zhang D. Influence of food safety concerns and satisfaction with government regulation on organic food consumption of Chinese urban residents [J]. Foods, 2022, 11(19): 2965.
[3] Min S, Peng J, Qing P. Does internet use improve food safety behavior among rural residents? [J]. Food Control, 2022, 139: 109060.
[4] Li X, Li X, Liao Y, et al. Analysis of residents’ food safety satisfaction from the perspective of income heterogeneity [J]. Scientific Reports, 2021, 11(1): 6666.
[5] Berghoff A S, Schur S, Füreder L M, et al. Descriptive statistical analysis of a real life cohort of 2419 patients with brain metastases of solid cancers [J]. ESMO open, 2016, 1(2): e000024.
[6] Ali S A G, AL-Fayyadh H R D, Mohammed S H, et al. A descriptive statistical analysis of overweight and obesity using big data [C]//2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022: 1-6.
[7] De Menezes F S, Liska G R, Cirillo M A, et al. Data classification with binary response through the Boosting algorithm and logistic regression [J]. Expert Systems with Applications, 2017, 69: 62-73.
[8] Unler A, Murat A. A discrete particle swarm optimization method for feature selection in binary classification problems [J]. European Journal of Operational Research, 2010, 206(3): 528-539.
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