Determinants of Vaccination Coverage and Hepatitis B Prevalence among Students at Gaston Berger University in 2024
Bibliographic record
Abstract
BACKGROUND: Hepatitis B is a public health problem. The objective of this work was to study the factors associated with the carriage of the hepatitis B surface antigen and vaccination coverage against this disease among students at the Gaston Berger University of Saint-Louis (UGB). METHODS: This is a cross-sectional study on a sample of 800 students. Data collection concerned epidemiological and serological data. The first came from an electronic questionnaire completed by self-administration. The second came from a rapid diagnostic test for hepatitis B surface antigen performed on each participant. Two operational variables were used: the vaccination rate and the vaccination coverage rate. The former is a ratio between those who have received at least one dose of the vaccine and those who are supposed to be vaccinated; the latter is a ratio between those who have received all three doses of the vaccine and those who are supposed to be vaccinated. Data analysis was carried out using R software. RESULTS: The average age was 23.3 ± 2.7 years and 96.8% of participants were single. The vaccination rate was 10.9% while the vaccination coverage rate was 5.0% with gender, faculty, level and residence as associated factors. The observed prevalence rate was 8.4% and the associated factors were sex and origin. The most affected residence was villages K (15.0%), A (12.9%) and F (11.4%). CONCLUSIONS: Hepatitis B is a reality within the UGB. Awareness-raising activities and vaccination operations for all unvaccinated residents are indicated to effectively combat this disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".