Evidence map›Paper›PMID 42133889›Full record

ArticleJMIR formative research2026

Decoding Anti-Substance Use Public Service Announcements: Content Analysis Grounded in the Elaboration Likelihood Model and Extended Parallel Process Model.

Jiawei Liu, Zhaomeng Niu

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jiawei Liu *Department of Communication Studies, Ernestine M. Raclin School of the Arts, Indiana University South Bend, South Bend, IN, 46615, United States.ORCID 0000-0002-3369-3355
Zhaomeng Niu *Health Informatics, School of Health Professions, Rutgers University, New Jersey, NJ, 08901, United States.ORCID 0000-0002-9906-5992

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tobacco, alcohol, and illicit drug use continue to pose substantial public health challenges in China. Although public service announcements (PSAs) are widely used for prevention, little is known about how these messages are constructed or the extent to which they draw on established health communication theories. Objective: This exploratory study aimed to characterize the design features of anti-substance use PSAs in China, assess their use of constructs from the extended parallel process model (EPPM) and the elaboration likelihood model (ELM), and compare patterns across anti-substance use PSAs. Methods: We conducted a content analysis of 89 publicly available anti-substance use PSAs produced in mainland China. Messages were identified via major Chinese video platforms and institutional websites and then screened using predefined eligibility criteria. Variables captured message source, intended audience, framing, substance depiction, cultural appeals, and EPPM and ELM components. Frequencies and proportions were calculated, and χ2 tests were used to examine differences by PSA type. To account for multiple comparisons, P values were adjusted using the Holm-Bonferroni correction. Results: Most PSAs did not identify a target audience (54/89, 60.7%), and public security departments were the most common sponsors (n=37, 41.2%), while none were sponsored by public health agencies. Theory use was selective: response efficacy (n=63, 70.8%) and perceived severity (n=55, 61.8%) appeared more often than self-efficacy (n=45, 50.6%) and perceived susceptibility (n=34, 38.2%); peripheral cues (n=79, 88.8%) were more common than central route cues (n=16, 18%). Differences across PSA types were observed in sponsorship, message features, and theoretical constructs. After adjustment for multiple comparisons, associations involving sponsoring organizations (public security departments and Chinese media) and perceived susceptibility remained statistically significant (all adjusted P=.01). Antidrug PSAs were predominantly associated with public security sponsorship, whereas antialcohol and antitobacco PSAs were more frequently linked to Chinese media sources. Perceived susceptibility cues were more common in antismoking PSAs than in antidrug PSAs, while other differences in framing, substance cues, cultural appeals, and ELM or EPPM constructs were not statistically significant after adjustment. Conclusions: Anti-substance use PSAs in China were characterized by limited audience segmentation and uneven use of theory-based persuasive strategies. Observed differences across alcohol-, tobacco-, and drug-focused messages suggest that PSA design may be shaped not only by partial application of communication theory but also by institutional influences and substance-specific contexts. These findings highlight the need for more context-sensitive and theory-informed approaches to anti-substance use PSA design in China.

Indexed as

Public Service Announcements as TopicSubstance-Related DisordersChinaHumansLikelihood Functionscontent analysiselaboration likelihood modelextended parallel process modelpublic service announcementsubstance use

Identifiers

PMID42133889
PMCPMC13175447

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.