| Literature DB >> 32750004 |
Hani Al-Dmour1, Ra'ed Masa'deh1, Amer Salman1, Mohammad Abuhashesh2, Rand Al-Dmour1.
Abstract
BACKGROUND: Despite the growing body of literature examining social media in health contexts, including public health communication, promotion, and surveillance, limited insight has been provided into how the utility of social media may vary depending on the particular public health objectives governing an intervention. For example, the extent to which social media platforms contribute to enhancing public health awareness and prevention during epidemic disease transmission is currently unknown. Doubtlessly, coronavirus disease (COVID-19) represents a great challenge at the global level, aggressively affecting large cities and public gatherings and thereby having substantial impacts on many health care systems worldwide as a result of its rapid spread. Each country has its capacity and reacts according to its perception of threat, economy, health care policy, and the health care system structure. Furthermore, we noted a lack of research focusing on the role of social media campaigns in public health awareness and public protection against the COVID-19 pandemic in Jordan as a developing country.Entities:
Keywords: COVID-19; Interventions; Jordan; awareness; behavior; behavioral change; coronavirus; pandemic; public health; public health protection; social media; social media platforms
Mesh:
Year: 2020 PMID: 32750004 PMCID: PMC7439806 DOI: 10.2196/19996
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Figure 1Diagram of the study model.
Variables and measurement items.
| Construct and measurement items | Description | |
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| SMP1 | Facebook helps me to recognize COVID-19a. |
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| SMP2 | Instagram helps me to recognize COVID-19. |
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| SMP3 | Twitter helps me to recognize COVID-19. |
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| SMP4 | WhatsApp helps me to recognize COVID-19. |
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| SMP5 | YouTube helps me to recognize COVID-19. |
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| PAW1 | Facebook contributes to increasing my awareness/knowledge of how to prevent COVID-19. |
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| PAW2 | Instagram contributes to increasing my awareness/knowledge of how to prevent COVID-19. |
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| PAW3 | Twitter contributes to increasing my awareness/knowledge of how to prevent COVID-19. |
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| PAW4 | WhatsApp contributes to increasing my awareness/knowledge of how to prevent COVID-19. |
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| PAW5 | YouTube contributes to increasing my awareness/knowledge of how to prevent COVID-19. |
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| PBC1 | Facebook contributes to changes in my behavior to prevent COVID-19 by taking various preventive measures (such as not shaking hands or kissing, not leaving the house, eating healthy food and vitamins, general hygiene, lack of anxiety and fear of disease, and increasing religious belief). |
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| PBC2 | Instagram contributes to changes in my behavior to prevent COVID-19 by taking various preventive measures (such as not shaking hands or kissing, not leaving the house, eating healthy foods and vitamins, general hygiene, lack of anxiety and fear of disease, and increasing religious belief). |
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| PBC3 | Twitter contributes to changes in my behavior to prevent COVID-19 by taking various preventive measures (such as not shaking hands or kissing, not leaving home, eating healthy foods and vitamins, general hygiene, lack of anxiety and fear of disease, and increasing religious belief). |
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| PBC4 | WhatsApp contributes to changes in my behavior to prevent COVID-19 by taking various preventive measures (such as not shaking hands or kissing, not leaving home, eating healthy food and vitamins, general hygiene, lack of anxiety and fear of the disease, and increasing religious belief). |
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| PBC5 | YouTube contributes to changes in my behavior to prevent COVID-19 by taking various preventive measures (such as not shaking hands or kissing, not leaving home, eating healthy food and vitamins, general hygiene, lack of anxiety and fear of the disease, and increasing religious belief). |
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| PPR1 | Social media platforms contribute to behavioral changes to protect me from infection with COVID-19. |
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| PPR2 | Social media platforms contribute to behavioral changes to protect others from infection with COVID-19. |
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| PPR3 | Social media platforms contribute to behavioral changes in educating others about infection with COVID-19. |
aCOVID-19: coronavirus disease.
Sample profile (N=2555), n (%).
| Characteristic | Value | |
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| Male | 1272 (49.8) |
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| Female | 1283 (50.2) |
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| 18-33 | 1196 (46.8) |
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| 34-43 | 627 (24.5) |
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| 44-53 | 447 (17.5) |
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| 54-63 | 206 (8.1) |
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| ≥64 | 79 (3.1) |
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| High school or less | 112 (4.4) |
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| Diploma | 262 (10.3) |
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| Bachelor’s degree | 1352 (52.9) |
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| Master’s degree | 421 (16.5) |
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| PhD | 520 (20.4) |
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| Irbid | 310 (12.1) |
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| Balqa | 123 (4.8) |
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| Jerash | 37 (1.4) |
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| Zarqa | 158 (6.2) |
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| Tafilah | 17 (0.7) |
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| Ajloun | 29 (1.1) |
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| Aqaba | 276 (10.8) |
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| Amman | 1219 (47.7) |
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| Karak | 227 (8.9) |
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| Madaba | 52 (2.0) |
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| Maan | 39 (1.5) |
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| Mafraq | 68 (2.7) |
Descriptive statistics of the research items and variables.
| Category | Mean (SD) | Level | Order | |
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| SMP1 | 3.84 (1.111) | High | 1 |
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| SMP2 | 3.07 (1.132) | Moderate | 5 |
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| SMP3 | 3.17 (1.133) | Moderate | 4 |
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| SMP4 | 3.54 (1.304) | High | 3 |
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| SMP5 | 3.81 (1.151) | High | 2 |
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| PAW1 | 3.86 (1.117) | High | 1 |
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| PAW2 | 3.17 (1.148) | Moderate | 5 |
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| PAW3 | 3.18 (1.131) | Moderate | 4 |
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| PAW4 | 3.58 (1.251) | High | 3 |
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| PAW5 | 3.71 (1.144) | High | 2 |
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| PBC1 | 3.94 (1.120) | High | 1 |
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| PBC2 | 3.28 (1.159) | Moderate | 4 |
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| PBC3 | 3.26 (1.139) | Moderate | 5 |
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| PBC4 | 3.67 (1.216) | High | 3 |
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| PBC5 | 3.72 (1.130) | High | 2 |
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| PPR1 | 3.99 (1.065) | High | 1 |
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| PPR2 | 3.99 (1.052) | High | 1 |
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| PPR3 | 3.96 (1.067) | High | 2 |
Overall means, SDs, levels, and orders of the study variables.
| Type and variable | Mean (SD) | Level | Order | |
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| Social media platforms | 3.4849 (0.84353) | High | 4 |
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| Public awareness | 3.5011 (0.88427) | High | 3 |
| Public behavioral change | 3.5754 (0.90706) | High | 2 | |
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| Public protection | 3.9808 (1.02517) | High | 1 |
Figure 2Measurement model showing the correlations among the four research variables.
Path analysis results for hypotheses 1 to 4. For all hypotheses, P<.001.
| Hypothesis | Path | Standardized effect (β) | Robust | Result |
| 1 | SMPa→PAWb | .823 | 64.128 (1544) | Supported |
| 2 | PAW→PBCc | .704 | 39.096 (1544) | Supported |
| 3 | PBC→PPRd | .465 | 16.134 (1544) | Supported |
| 4 | SMP→PPR | .149 | 5.301 (1544) | Supported |
aSMP: social media platforms.
bPAW: public awareness.
cPBC: public behavioral change.
dPPR: public protection.
Figure 3Estimated path values for the hypothesized structural model.