Assessing the level of knowledge and available sources of information about hepatitis C infection among HCV-infected Egyptians
Bibliographic record
Abstract
BACKGROUND: Egypt has the largest proportion of hepatitis C virus (HCV) infection worldwide and there is an urgent need to increase community awareness and knowledge about the disease in the country. The main aim of this study was to assess the level of knowledge and awareness about HCV in clinically diagnosed HCV patients in Egypt. METHODS: This was a prospective, cross-sectional study conducted between 1 February 2014 and 30 April 2014 in Cairo, Egypt using validated questionnaire as an instrument for data collection. A structured questionnaire was developed based on similar published surveys. Data collected included demographic characteristics, exposure to the disease, health insurance status, the source of medical information, and knowledge of different routes of transmission; a point was given for each correct answer with a possible score of 0 to 12. RESULTS: A total of 203 patients took part in this study with a response rate of 90%. Most-142 (70%)-were married, 119 (63%) were unemployed, 127 (62.9%) were aged above 50 years, 88 (45.1%) were living in Cairo, and 45 (22.4%) had a college degree. Half of the participants believed that HCV infection is not transmitted through sex, while 79 (39.9%) did not know that HCV could be transmitted from a mother to her infant during labor. A quarter of participants believed that HCV vaccine is available, and 45 (24.6%) never knew if their treatment was successful. The median knowledge score of HCV infection in the survey was 7.5; 100 (50.3%) participants had ≤ median knowledge score of HCV infection. Logistic regression analysis showed a duration of infection (OR 1.647, CI 1.189-2.82) and the participants who visited physicians when only they felt sick were less likely to have the above median knowledge score (> 7.5) of HCV infection (OR 0.41, 95% CI 0.19-0.87). CONCLUSIONS: Considering the unsatisfactory level of HCV knowledge among infected patients, Egyptian healthcare authorities should organize national awareness campaigns encouraging HCV testing based on educational interventions and activities to improve the level of knowledge. More investment in research is also needed to limit the further growth of the HCV disease burden in Egypt.
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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 | 0.003 |
| 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".