Research on gambling has the double mandate of public service and the advancement of science. This paper is meant to carry forward that mandate. Latent in research on the causes of problem gambling is the policy insight that these causes represent different types of phenomena and are unequally mutable to practitioners' efforts to prevent and/or treat problem gambling. By making the issue of mutability manifest in research, findings from research would have more policy relevance and practical import. Data from a 1989 Iowa survey on lottery play and problem gambling are analyzed to illustrate this point. 1,226 respondents were contacted by phone and phone interviews were completed with 1,011 of these 1,226 eligible respondents. With multiple regression, we assessed the contributions of mutable and immutable variables to the explained variance in problem gambling. The results show mutable correlates explain enough variance in problem gambling to recommend their consideration in treatment/prevention. The results also suggest a social as well as a psychological etiology to problem gambling. Future research should, however, do a more complete comparison of social and psychological causes of problem gambling.This research was partially supported by the Iowa Department of Human Services and the National Institute of Mental Health (1 R01 MH50369-01A1). The authors wish to thank Willis Goudy, anonymous readers and the editor of this journal for helpful comments. 相似文献
Hypertension and its related complications could be a major threat issue for cardiopathy and stroke. Effective prevention and control can decrease the incidence rate of complications in hypertension. Based on the medical data of 3062 patients with cardiovascular and cerebrovascular diseases from 2017 to 2018 in a grade-A tertiary hospital in Shanghai, the study identified the risk factors of hypertension complications by text mining. On this basis, the K2 algorithm based on the improved particle swarm optimization was proposed to optimize the structure of the Bayesian network (BN) by establishing a multi-population cooperative search mechanism. Then the optimized BN was used to analyze and predict the incidence rate of hypertension complications. Results indicate that the major indicators of accuracy, sensitivity, specificity, and AUC have been improved, and the proposed algorithm is superior to the common data mining algorithms such as the back propagation neural network and the decision tree. Through the proposed model and algorithm, the high-risk factors were identified and the occurrence probability of hypertension complications was predicted, which could provide the personalized health management guidance for hypertensive patients to prevent and control hypertension complications.
