Perceptions of, and Obstacles to, SARS-CoV-2 Vaccination Among Adults in Lebanon: Cross-sectional Online Survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is an additional burden on Lebanon's fragmented health care system and adds to its ongoing political, economic, and refugee crises. Vaccination is an important means of reducing the impact of the pandemic. OBJECTIVE: Our study's aims were to (1) assess the prevalences of intention to vaccinate and vaccine hesitancy in Lebanon; (2) determine how vaccine hesitancy in Lebanon varies by sociodemographic, economic, and geographic characteristics; and (3) understand individuals' motivations for vaccinating as well as concerns and obstacles to vaccination. METHODS: We performed a cross-sectional study from January 29, 2021, to March 11, 2021, using an online questionnaire of open- and closed-ended questions in Arabic via convenience "snowball" sampling to assess the perceptions of adults residing in Lebanon. RESULTS: Of the 1185 adults who participated in the survey, 46.1% (95% CI: 43.2%-49.0%) intended to receive the SARS-CoV-2 vaccine when available to them, 19.0% (95% CI 16.8%-21.4%) indicated they would not, and 34.0% (95% CI 31.3%-36.8%) were unsure (with an additional 0.9% skipping this question). The most common reasons for hesitancy were concerns about safety, limited testing, side effects, and efficacy. Top motivations for vaccinating were to protect oneself, protect one's family and the public, and end the pandemic. Despite financial hardships in Lebanon, barriers to vaccine access were not frequently described as concerns. Established health care facilities, rather than new temporary vaccination centers, were most frequently selected as preferred vaccination sites. CONCLUSIONS: Vaccine hesitancy appears to be high in Lebanon. Disseminating clear, consistent, evidence-based safety and efficacy information on vaccines may help reduce vaccine hesitancy, especially among the large proportion of adults who appear to be unsure about (rather than opposed to) vaccination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".