Picture analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic
Bibliographic record
Abstract
Today, advertising science is a tool that helps advertisers to design their advertising to meet the needs of the audience. In this regard, knowing and understanding the audience is one of the most important points that advertisers should pay attention to before advertising in order to better attract the audience. This study has been done with the aim of billboards and infographics analysis related to promoting preventive behaviors and vaccination against the Coronavirus disease pandemic and investigating the opinion of the general adult population of Iran. The method used in this research is the qualitative method. In this research, according to the type of data and research goals, Kress and Van Leeuwen's discourse theory method has been used. The sample size includes 36 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). Then, an online survey about promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach's alpha internal consistency coefficient, yielded a coefficient of 0.968. In this study, data analysis was conducted using IBM SPSS Statistics software, version 15.0 (IBM Corp., Armonk, NY, USA). Finally, users' opinions about of billboards and infographics were analyzed using descriptive statistics. The results of component analysis and surveys show that visual components such as «The staring look at the spectator (Demand)», «Head-on Shot (inclusion)», «Down Shot (Creating a sense of participation for the represented person)», «Close-up (intimate/individual relationship)», «Level Shot (equality)» and «High-Angle Shot (Presenting power)» in medical advertising has had a great impact in arousing public opinion to create a positive attitude towards preventive measures and vaccination during the Coronavirus disease epidemic. The results of this research show that in visual communication, visual components play a significant role in creating and maintaining target ideologies. Also, advertising in the field of preventive measures in medical sciences requires certain rules that determine people's culture and the main foundation of their attitude and thinking. Therefore, it is necessary to know such knowledge and learn it by the medical staff to deal with critical situations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".