Socio-Demographic Characteristics of Health Care Workers and Hepatitis B Virus (HBV) Infection in Public Teaching Hospitals in Khartoum State, Sudan
Bibliographic record
Abstract
BACKGROUND: HBV is second to tobacco as a known human carcinogen and the 10th leading cause of death worldwide. OBJECTIVES: To examine the socio-demographic characteristics of health care workers and hepatitis B virus in Public Teaching Hospitals in Khartoum State, Sudan, in 2004. METHODS: It was an observational, cross sectional, facility-based study. A total of 843 subjects were selected. It was conducted through multistage cluster sampling. The clustering was based on: type of hospital (Federal or State) and degree of exposure (type of department). For the analysis, Z-test for single proportion and some non-parametric tests such as Chi-Square test were used. RESULTS: Among the 843 subjects tested for HBV markers (Anti-HBc, HBsAg, HBsAb, and HBeAg), the prevalence of Anti-HBc, HBsAg, HBsAb, and HBeAg was found to be 57%, 6%, 37% and 9% respectively. Seroprevalence of all HBV markers was found to be statistically significant with demographic factors (P<0.05). CONCLUSION: Infection rate, carrier rate and a profile of high infectivity rate were found to be high. The immunity rate was low. There is a significant association between HBV markers and socio-demographic characteristics. Highest rate of infection was found in State Hospitals, South and West regions, married HCWs and HCWs of age group 30-49.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".