Which Images and Features in Graphic Cigarette Warnings Predict Their Perceived Effectiveness? Findings from an Online Survey of Residents in the UK
Which Images and Features in Graphic Cigarette Warnings Predict Their Perceived Effectiveness? Findings from an Online Survey of Residents in the UK
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DOI：
10.1007/s12160-015-9693-4
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发表时间：
2015-10-01
影响因子：
3.8
通讯作者：
Williams, Brian
中科院分区：
文献类型：
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作者：
Cameron, Linda D.;Williams, Brian
背景许多国家正在实施香烟的图形警告。哪些图形特征影响其有效性仍不清楚。目的为了确定图形警告的特征，预测其在劝阻吸烟方面的感知效果。方法在对健康威胁反应的常识模型的指导下，我们对42个图形警告的疾病风险表征和媒体特征（例如，照片、隐喻）。从15,536调查参与者的数据，我们进行了分层逻辑回归测试的属性预测参与者选择的警告作为effective.Results患病的身体部位的图像预测更大的感知效果; OR = 6.53-12.45跨吸烟状态（吸烟者，前吸烟者，年轻的非吸烟者）组。增加感知效果的特征包括死亡或生病的人，儿童和医疗技术的图像;关注癌症;和照片。属性降低感知的有效性包括不孕/阳痿，成瘾，香烟化学品，化妆品外观，戒烟自我效能，和metaphors.Conclusions这些研究结果的代表性和媒体属性预测感知的有效性可以通知策略生成图形警告。
Background Many countries are implementing graphic warnings for cigarettes. Which graphic features influence their effectiveness remains unclear.Purpose To identify features of graphic warnings predicting their perceived effectiveness in discouraging smoking.Method Guided by the Common-Sense Model of responses to health threats, we content-analyzed 42 graphic warnings for attributes of illness risk representations and media features (e.g., photographs, metaphors). Using data from 15,536 survey participants, we conducted stratified logistic regressions testing which attributes predict participant selections of warnings as effective.Results Images of diseased body parts predicted greater perceived effectiveness; OR = 6.53-12.45 across smoking status (smoker, ex-smoker, young non-smoker) groups. Features increasing perceived effectiveness included images of dead or sick persons, children, and medical technology; focus on cancer; and photographs. Attributes decreasing perceived effectiveness included infertility/impotence, addictiveness, cigarette chemicals, cosmetic appearance, quitting self-efficacy, and metaphors.Conclusions These findings on representational and media attributes predicting perceived effectiveness can inform strategies for generating graphic warnings.