Challenges for the smoking ban in Israeli pubs and bars: analysis guided by the behavioral ecological model

以色列酒吧禁烟面临的挑战:基于行为生态模型的分析

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Abstract

BACKGROUND: The latest amendment to the ban on smoking in public places in Israel was implemented in 2007, adding pubs and bars (P&B) to the list of public places in which smoking is prohibited. However, smoking in most P&B continued. The aim of the study was to identify the theoretically plausible reasons for the partial success of a public ban on smoking in P&B settings. Explanations provided by P&B owners were interpreted as probable causal factors based on the Behavioral Ecological Model (BEM). METHODS: Qualitative interviews were performed with 36 P&B owners in Tel-Aviv and 18 Israeli towns and cities of various population size. RESULTS: P&B owners reported a variety of situational factors (i.e., contingencies) and reinforcers as likely explanations of the partial failure of the legislated ban on smoking in public places, particularly P&B. The major reinforcers for non-adherence with the law were no or low frequency of inspections and low penalties from authorities. P&B owners also feared loss of customers and revenue if bans were enforced in their own establishment but not in competing establishments. Finally, owners reported social norms prevailing among some Israeli patrons supporting smoking in P&B settings, in part to express opposition to the new law. CONCLUSIONS: Qualitative assessment can uncover probable social situations that operate to prevent greater adherence to smoking bans. The results warrant confirmation by quantitative analyses. Policies with mandated inspections and penalty requirements that are implemented in all bars without prejudice could lead to greater adherence to smoking bans. Positive reinforcing consequences that encourage adherence (such as publicity and support from non-smokers) would be more likely to generate both greater adherence to the policy and good will toward the government. Principles of behavior outlined in the BEM offer guidance for designing quantitative confirmation analyses of future bans.

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