印象作为烟草公众教育活动评估中的媒体剂量指标的有效性:观察研究。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-11-05 DOI:10.2196/55311
Kevin Davis, Laurel Curry, Brian Bradfield, David A Stupplebeen, Rebecca J Williams, Sandra Soria, Julie Lautsch
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引用次数: 0

摘要

背景:评估研究越来越需要目标收视点或总收视点以外的其他方法来全面衡量现代多渠道公共教育活动的总曝光率,这些活动使用多种渠道,包括电视、广播、数字视频和付费社交媒体等。收视率数据通常只反映广播媒体(电视和广播)的传播情况,不包括其他渠道。需要进行研究,以验证客观的跨渠道指标,如印象与自我报告的活动信息曝光率:本研究旨在探讨在加利福尼亚州,较高的媒体活动总印象量是否能预测个人层面的自我报告活动曝光量:我们分析了加利福尼亚州烟草预防计划从 2019 年 8 月到 2022 年 12 月的全州烟草教育活动中超过 3 年的广告印象。不同指定市场区域 (DMA) 和不同时间段的印象数据各不相同。这些数据与来自 45 波 18-55 岁加州人面板调查数据(N=151,649)的个人受访者进行了合并。根据受访者的 DMA 和完成调查的时间将印象合并到受访者中。我们使用逻辑回归法估算了受访者回忆起活动的几率,并将其作为向每位受访者的 DMA 发送的累计印象和过去 3 个月印象的函数:结果:累计印象与 "味道勾起孩子们的回忆 "活动的回忆率呈显著正相关(几率比 [OR] 1.15,PC结论):加利福尼亚州烟草预防计划的长期多渠道烟草教育活动的累积印象差异可预测自我报告的活动回忆率的增加,这表明印象可能是潜在活动接触的有效替代物。使用印象来评估公众教育活动可能有助于解决目前因现代多渠道媒体活动的零散性而产生的方法论局限性。
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The Validity of Impressions as a Media Dose Metric in a Tobacco Public Education Campaign Evaluation: Observational Study.

Background: Evaluation research increasingly needs alternatives to target or gross rating points to comprehensively measure total exposure to modern multichannel public education campaigns that use multiple channels, including TV, radio, digital video, and paid social media, among others. Ratings data typically only capture delivery of broadcast media (TV and radio) and excludes other channels. Studies are needed to validate objective cross-channel metrics such as impressions against self-reported exposure to campaign messages.

Objective: This study aimed to examine whether higher a volume of total media campaign impressions is predictive of individual-level self-reported campaign exposure in California.

Methods: We analyzed over 3 years of advertisement impressions from the California Tobacco Prevention Program's statewide tobacco education campaigns from August 2019 through December 2022. Impressions data varied across designated market areas (DMAs) and across time. These data were merged to individual respondents from 45 waves of panel survey data of Californians aged 18-55 years (N=151,649). Impressions were merged to respondents based on respondents' DMAs and time of survey completion. We used logistic regression to estimate the odds of respondents' campaign recall as a function of cumulative and past 3-month impressions delivered to each respondent's DMA.

Results: Cumulative impressions were positively and significantly associated with recall of each of the Flavors Hook Kids (odds ratio [OR] 1.15, P<.001), Dark Balloons and Apartment (OR 1.20, P<.001), We Are Not Profit (OR 1.36, P<.001), Tell Your Story (E-cigarette, or Vaping, product use Associated Lung Injury; OR 1.06, P<.05), and Thrown Away and Little Big Lies (OR 1.05, P<.01) campaigns. Impressions delivered in the past 3 months were associated with recall of the Flavors Hook Kids (OR 1.13, P<.001), Dark Balloons and Apartment (OR 1.08, P<.001), We Are Not Profit (OR 1.14, P<.001), and Thrown Away and Little Big Lies (OR 1.04, P<.001) campaigns. Past 3-month impressions were not significantly associated with Tell Your Story campaign recall. Overall, magnitudes of these associations were greater for cumulative impressions. We visualize recall based on postestimation predicted values from our multivariate logistic regression models.

Conclusions: Variation in cumulative impressions for California Tobacco Prevention Program's long-term multichannel tobacco education campaign is predictive of increased self-reported campaign recall, suggesting that impressions may be a valid proxy for potential campaign exposure. The use of impressions for purposes of evaluating public education campaigns may help address current methodological limitations arising from the fragmented nature of modern multichannel media campaigns.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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