| Literature DB >> 35153094 |
Muhammad Yousaf1, Syed Hassan Raza2, Nasir Mahmood3, Rachel Core4, Umer Zaman5, Aqdas Malik6.
Abstract
Renewed COVID-19 outbreaks, stemming from the highly infectious Delta and Omicron variants, prompted rising fears of a 'pandemic among the unvaccinated'. To address this prevalent vaccination crisis, media framing communication strategies can amplify the scientific evidence on COVID-19 vaccines to reach diverse geographic and socio-economic communities. The critical role of media framing strategies to engage and encourage large populations regarding vaccine acceptance has been rarely studied, despite growing evidence on vaccine hesitancy. The present study used a multi-method approach (i.e., content analysis and quasi-experiments) that unpacked the framing practices employed by the mainstream media in Pakistan. The findings of the content analysis revealed that the media extensively used uncertainty, conflict, consequences, and action rather than new evidence and reassurance frames in its COVID-19 related campaigns. In a series of quasi-experiments involving 720 participants, we manipulated these six frames of COVID-19 related news coverage (i.e., uncertainty, conflict, consequences, action, new evidence, and reassurance) to investigate the underlying mechanism that influences vaccine acceptance. The findings established that the message-consistent effects of media frames manifesting fear (e.g., consequence and uncertainty) and action cues made receivers more supportive of vaccination. The present study findings theoretically address the calls for a more inclusive "community-health reporting model", besides offering new evidence on the media framing strategies to deliver more targeted, meaningful, and effective campaigns to raise public acceptance for COVID-19 vaccines.Entities:
Keywords: COVID-19 vaccination; Community health; Framing theory; Media; Public acceptance for vaccination; Vaccine hesitancy
Mesh:
Substances:
Year: 2022 PMID: 35153094 PMCID: PMC8806129 DOI: 10.1016/j.vaccine.2022.01.055
Source DB: PubMed Journal: Vaccine ISSN: 0264-410X Impact factor: 4.169
Six Frames and their definitions.
| This emphasizes the consequences of the illness, including human life; social impact is the focal point of the story. | |
| Uncertainties may be portrayed regarding any aspect of the epidemic, including the cause, cure, and possible spread. Also included is the portrayal of the disease as something obscure that needs more exploration and assessment by the government or scientific bodies. | |
| The story focuses on any action against the disease, including anticipation, potential, solution and strategies. | |
| The story communicates the possibility that people should not be stressed or worried about the effects of the disease. Additional stories cover the readiness and successes of authorities in fighting the infection. | |
| The story is about arguments, disagreements and different ideas among news sources. Alternately, it could be discussion and debate on how to combat the disease effectively. | |
| This frame is related to new findings and results or explores new evidence that helps advance the understanding of the disease. It also discloses new strains of the infection, new approaches for spreading, new technologies to prevent, cure, treat the disease, and the development of new medicines. |
Comparison between the six News Media Frames.
| Media | Frames | ||||||
|---|---|---|---|---|---|---|---|
| Consequence | Uncertainty | Action | Reassurance | Conflict | New Evidence | Total | |
| Articles | 205 (17.27%) | 516 (43.47%) | 113 9.52%) | 65 (5.48%) | 247 (20.81%) | 41 (3.54%) | 1187 |
Convergent Validity.
| MA1 | 0.80 | 0.883 | 0.69 | 0.828 | 0.78 | 0.870 | 0.69 | 0.810 | 0.81 | 0.887 | 0.72 | 0.813 |
| MA2 | 0.879 | 0.823 | 0.873 | |||||||||
| MA3 | 0.832 | 0.859 | 0.864 | |||||||||
| BP1 | 0.78 | 0.868 | 0.72 | 0.821 | 0.80 | 0.880 | 0.71 | 0.832 | 0.77 | 0.869 | 0.68 | 0.857 |
| BP2 | 0.795 | 0.832 | 0.806 | |||||||||
| BP3 | 0.869 | 0.865 | 0.811 | |||||||||
| RP 1 | 0.71 | 0.837 | 0.63 | 0.827 | 0.73 | 0.841 | 0.64 | 0.821 | 0.74 | 0.803 | 0.58 | 0.803 |
| RP 2 | 0.785 | 0.826 | 0.745 | |||||||||
| RP 3 | 0.773 | 0.746 | 0.728 | |||||||||
| PAV1 | 0.84 | 0.902 | 0.75 | 0.886 | 0.82 | 0.891 | 0.73 | 0.891 | 0.85 | 0.912 | 0.77 | 0.888 |
| PAV2 | 0.896 | 0.871 | 0.911 | |||||||||
| PAV3 | 0.822 | 0.802 | 0.842 | |||||||||
RP = Risk Perception, BP = Benefit Perception, MA = Media Attention, and PAV = Public acceptance of COVID-19 Vaccine, L = item loading, CR = Composite Reliability, and AVE = Average Variance Extracted.
Discriminant validity: Fornell-Larcker Criterion.
| BP | BP | BP | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.82 | 0.84 | 0.82 | ||||||||||
| MA | 0.29 | 0.84 | 0.46 | 0.83 | 0.41 | 0.85 | ||||||
| 0.27 | 0.30 | 0.86 | 0.38 | 0.47 | 0.85 | 0.23 | 0.44 | 0.88 | ||||
| RP | 0.22 | 0.41 | 0.43 | 0.79 | 0.27 | 0.35 | 0.50 | 0.79 | 0.18 | 0.37 | 0.43 | 0.76 |
* p < 0.05 ** p < 0.001.
Meditation results.
| p | p | P | |||||
|---|---|---|---|---|---|---|---|
| Consequence | 0.104 | 0.00 | 0.147 | 0.00 | 0.049 | 0.00 | 0.228 |
| Uncertainty | 0.281 | 0.00 | 0.130 | 0.00 | 0.068* | 0.19 | 0.379 |
| Action | 0.302 | 0.00 | 0.116 | 0.00 | 0.021* | 0.5 | 0.278 |
| Reassurance | 0.058* | 0.58 | 0.157 | 0.00 | 0.019* | 0.43 | 0.160 |
| Conflict | −0.03* | 0.73 | 0.134 | 0.05 | 0.061* | 0.08 | 0.161 |
| New Evidence | 0.145 | 0.13 | 0.161 | 0.00 | 0.037 | 0.12 | 0.340 |
β = Standardized Regression Weight and * p ≤ 0.05.
Fig. A1Consequence.
Fig. A2Uncertainty.
Fig. A3Action.
Fig. A4Reassurance.
Fig. A5Conflict.
Fig. A6New Evidence.