Global Systematic Review of the Measurement of Stigma Associated With People Living With Hepatitis B or Hepatitis C Viruses
Bibliographic record
Abstract
Chronic hepatitis B and C affect over 300 million people globally. Despite treatment advances, stigma towards people living with hepatitis B/C (PLWHB/C) remains a barrier to care and impacts health outcomes. Addressing this stigma is key to achieving hepatitis elimination goals. This systematic review aims to synthesise existing approaches to measuring stigma experienced by PLWHB/C and examine factors associated with stigma in different social contexts. Databases searched included PubMed, PsycInfo and Web of Science, as well as grey literature (01/01/2008-30/06/2023). Studies were included if stigma experienced by or directed towards PLWHB/C was measured quantitatively. Data from included studies were synthesised using a narrative approach. Among 3053 studies, 81 were included. Various tools were used to measure internalised (e.g., self-blame, shame), enacted (e.g., experiences of discrimination) and anticipated stigma (e.g., expectations of discrimination) related to PLWHB/C; most commonly the Toronto Chinese Hepatitis B Stigma Scale and Brener and Von Hippel's tool. Stigma was highly prevalent, impacting psychosocial wellbeing, treatment-seeking behaviours and quality of life. Lower knowledge and conservative beliefs were linked to higher public stigma. Educational interventions and stigma-reducing media showed some benefit in mitigating stigmatising attitudes. The review highlights stigma's pervasive nature and detrimental psychosocial impacts for PLWHB/C globally. While diverse measurement tools were used, standardising culturally validated instruments aligned with conceptual frameworks could improve research. Tailored educational initiatives could help reduce stigmatising attitudes. Crucially, stigma hindered timely diagnosis and treatment access, emphasising the need for multi-level interventions addressing stigma to achieve elimination goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".