An evolutionary concept analysis: stigma among women living with hepatitis C
Bibliographic record
Abstract
BACKGROUND: Stigma is a complex social phenomenon that leads to marginalization and influences the course of illness. In the context of hepatitis C virus (HCV), stigma is a well-documented barrier to accessing care, treatment, and cure. In recent years, HCV rates among women have increased, resulting in an urgent need to address stigma and its harmful effects. The purpose of this concept analysis was to investigate stigma in the context of women living with HCV using Rodgers' evolutionary method. METHODS: PubMed, CINAHL, Scopus, Medline, PsycINFO, and Nursing and Allied Health were used to identify articles describing HCV stigma among women. Articles from peer-reviewed journals and geographic locations, published between 2002-2023, were included in the analysis. As specified in Rodgers' evolutionary method, articles were analyzed with a focus on the concept's context, surrogate and related terms, antecedents, attributes, examples, and consequences. RESULTS: Following screening, 33 articles were selected for inclusion in the analysis. Discrimination and marginalization were identified as surrogate and related terms to stigma; and antecedents of stigma were identified as limited knowledge, fear of diagnosis, and disclosure. Prevalent attributes of stigma in the literature were described as feelings of decreased self-worth, negative stereotyping, and fear of transmission. Importantly, HCV stigma among women is unique in comparison to other forms of infectious disease-related stigma, primarily due its impact on women's identity as mothers and caregivers. Stigmatization of women living with HCV resulted in negative consequences to personal relationships and healthcare access due to decreased health-seeking behaviours. Although access to HCV treatment has changed considerably over time, a temporal analysis could not be completed due to the limited number of articles. CONCLUSIONS: Stigma in the context of women living with HCV has its own unique antecedents, attributes, and consequences. This enhanced understanding of stigma among women living with HCV has the potential to inform improved and more effective approaches to care, which will be required to reach HCV elimination. Furthermore, this analysis identifies stigma layering and stigma in the direct-acting antiviral treatment era as areas for more in-depth future inquiry.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".