Bibliographic record
Abstract
Individuals infected with Hepatitis C virus are often stigmatized, a situation shaped through unique socio-political, economic, physical and linguistic factors. Socio-politically the association of Hepatitis C with injection drug use and the pervasive stigma present in not only the general population but within the health care system marginalizes patients. Patients often feel as though they are to blame and treatment will be “rationed”. The economics factors affecting those with Hepatitis C serve to further limit access to treatment. Underscoring these factors is a unique linguistic discourse that draws on the language of biomedicine, obscuring the experiences of individuals affected with this disease. Finally, the physical setting of Hepatitis C treatment can further entrench the stigma, and subsequently health care access. Physical spaces play into the power dynamic, even with well-intentioned treatment strategies such as locating Hepatitis C treatment within opiate substitution clinics. As nurses it is crucial to be aware of and address the full relational context of a disease in order to minimize stigma and enhance equitable treatment. Strategies to help nurses act relationally and advocate for the best interest of clients are presented in this paper.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".