Seroprevalence of Hepatitis B Among Municipal Waste Collectors in Penang Island, Malaysia and Their Knowledge, Attitude and Practice Towards the Prevention of Hepatitis B
Bibliographic record
Abstract
While there are many risks that increase an individuals’ exposure to infectious diseases such as Hepatitis B, occupational exposure increases one’s risk to acquire such infections. The objective of this study was to determine the prevalence of Hepatitis B among municipal solid waste collectors and their knowledge, attitude and practice towards the prevention of Hepatitis B. An analytical cross-sectional study was carried out among solid waste collectors in Penang Island from November 2017 to Mei 2018. The inclusion criteria of this study were solid waste collectors who have been working for at least 6 months and able to understand either Malay or English language. Those who did not turn up during the data collection period and who were unable to give their blood sample were excluded from the study. Participants’ blood were tested for Hepatitis B surface antigen (HBsAg) and Hepatitis B surface antibody (HBsAb) using Elisa. The waste collectors were then interviewed using a structured questionnaire. Ethical approval was obtained from the Institutional Research Ethics Committee, PMC RC-14. A total of 184 out of 221 eligible waste handlers participated in the study, giving a response rate of 83.3%. The prevalence of Hepatitis B in this study was 1.6%. Significant correlation was observed between attitude and practice scores (r=0.203, p=0.006). The prevalence of Hepatitis B among municipal waste collectors was low in this study. Most had poor knowledge in regards to Hepatitis B. Empowering workers by providing them adequate information is essential to reduce the risk of contracting the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".