Shaping North-African Public Health Decisions: A Latent Class Analysis of Social Media's Influence on Attitudes and Behaviors Towards COVID-19 Vaccines
Bibliographic record
Abstract
Background: The global crisis brought on by the COVID-19 pandemic highlighted the crucial role of vaccines in public health. However, the success of vaccination campaigns is not solely determined by the availability of vaccines but also by public willingness to receive them. In North Africa, the variability in vaccine acceptance has raised concerns, drawing attention to the need for understanding the factors influencing public attitudes. Objectives: To identify the impact of the information consumption modalities related to the Coronavirus Disease 2019 (COVID-19) pandemic and its vaccines, on the vaccine uptake decision among social media users. Also, to study the relationships between vaccination attitudes, and latent subgroups, in terms of socio-demographic variables, fear of COVID-19 and perceived stress. Method: A total of 723 subjects (males: 48.8%; mean±standard deviation of age: 31±11 years), participated in our survey prepared online on the Google Forms application via the platforms Twitter and Facebook. Results: Five latent classes were identified by the analysis: Class 1 (mixed consumers), class 2 (largest consumers of social media), class 3 (consumers of official information), class 4 (low consumers of information on the vaccine), and class 5 (social media consumers and information verifiers). The subgroup that is knowledgeable about COVID-19 pandemic and its vaccines, and which consumes the most information about the vaccine from official sources, is the one with the highest vaccine acceptance rate. In addition, the hesitant attitude towards the COVID-19 vaccine was linked to gender and mask wearing, while refusal behavior was linked to age, female gender, education level, mask wearing, and fear of COVID-19. Conclusion: This study's investigation into the impact of social media on public attitudes and behaviors towards COVID-19 vaccines in North Africa has significant implications for both public health strategy and policy. By identifying distinct latent classes based on social media usage patterns, the research reveals a complex landscape of factors influencing vaccine hesitancy in the region. The nuanced understanding derived from these findings is crucial for the development of more effective public health messaging, tailored to address the specific concerns and misinformation trends prevalent within each identified group. The insights gained from this study can guide policymakers in allocating resources more effectively, particularly in areas exhibiting higher levels of vaccine hesitancy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".