Hazards Analysis, within Departments and Occupations, for Hepatitis B Virus among Health Care Workers in Public Teaching Hospitals in Khartoum State; Sudan
Bibliographic record
Abstract
BACKGROUND: Infection with hepatitis B virus (HBV) can lead to a range of clinical illnesses. OBJECTIVES: To examine hazards of hepatitis B virus associated with clinical departments and occupations; among health care workers in Public Teaching Hospitals in Khartoum State, Sudan. METHODS: The study was a cross sectional, facility-based study. It was conducted on stratified two-stage cluster random sample of 843 subjects of whom 324 were at high-hazard, 445 at moderate hazard, and 74 at low hazard; depending on degree of exposure to blood and body fluids of patients. To assess hazards of HBV among departments and occupations of HCWs, non-parametric Methods of Chi-square test, was used. RESULTS: For Anti-HBc vulnerable departments was Renal Dialysis (100%); while for occupations was midwives (73.3%). For carrier rate (+ve HBsAg), highest rate found in department of Management (6.8%); while for occupations was Midwives (6.7%). Regarding immunity (+ve Anti-HBs), the highest percentage found in the department of Dentistry (25.9%); while for occupations was associated with Doctors (14.8%). For a profile of high infectivity (+ve HBeAg), the most vulnerable department in terms of HBV hazards was the Surgery (1.4%); while for occupations was nurses (0.9%). CONCLUSION: There was a significant association for infection rate of HBV with occupation and type of department. The most hazardous departments, was Surgery with a profile of high infectivity rate, followed by other departments (medicine, pediatrics, psychiatry & ophthalmology). As for occupations, the most hazardous group was nurses group with a profile of high infectivity rate.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".