Perceptions of Stigma Among Patients With Hepatitis B in Germany: Cross-Sectional Survey
Bibliographic record
Abstract
Background: Many studies find associations between hepatitis B and stigma, but studies from the Western European context are lacking. Based on available studies, we hypothesized that younger age, higher education, male gender, higher privacy needs, and non-German mother tongue were positively associated with perceived hepatitis B-related stigma. Objective: This study aims to describe the prevalence of perceived social stigma among patients with hepatitis B in Germany and to assess what factors are associated with perceptions of hepatitis B-related stigma. Methods: Applying the short version of the Berger stigma scale, we surveyed 195 patients with hepatitis B about their perceptions of hepatitis B-related stigma, privacy needs, and demographic variables through a paper-based questionnaire. Venue-based recruitment of adult patients diagnosed with acute or chronic hepatitis B was implemented at 3 clinical centers in Germany. Patients who could not read German were excluded from the study. Results: From the 195 valid questionnaires, 45.1% (88/195) of participants identified as female, 36.6% (71/195) had a high school diploma, and 56.9% (111/195) reported a mother tongue other than German. The mean (SD) stigma score throughout the sample was 5.52 (6.02; range 0-24) and the median was 3.50 (IQR=9.75). Regression analysis revealed that non-German mother tongue, individual data privacy needs, and participants' secrecy regarding their hepatitis B diagnosis independently predicted perceived hepatitis B-related stigma. More precisely, the higher the data privacy need and the more secret the hepatitis B diagnosis, the higher the perceived stigma, and perceived stigma was higher for patients with a non-German mother tongue. Age, gender, and education were no predictors of perceived stigma. Conclusions: The surveyed patients with hepatitis B in Germany reported lower levels of hepatitis B-related stigma than found in other studies conducted in Asian countries. The association with non-German mother tongue indicates an important cultural and social component in the perception of stigma. Community-based interventions and the sensibilization of health care professionals might help overcome perceptions of stigma among hepatitis B-affected populations.
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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 | 0.000 | 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".