Has the Experience of Hepatitis C Diagnosis Improved Over the Last Decade? An Analysis of Canadian Women’s Experiences
Bibliographic record
Abstract
Background In Canada, incidents of new hepatitis C virus infections are rising among women aged 15-29 years and now comprise 60% of new infections among this age group. A negative diagnosis experience continues to be a problem affecting women living with hepatitis C virus. With new effective treatments, nurses will have more involvement in hepatitis C virus care and diagnosis, which is a critical time to facilitate appropriate education and management. Purpose This study explored Canadian women's experience of hepatitis C virus diagnosis in order to develop recommendations to improve care at the point of diagnosis. Methods Purposive sampling was used to recruit and interview 25 women. Using narrative inquiry, we examined Canadian women's experience of hepatitis C virus diagnosis. Results Women's diagnosis experiences were shaped by the context of diagnosis, factors prompting the testing, the testing provider, and information/education received. The context of diagnosis foreshadowed how prepared women were for their results, and the absence of accurate information magnified the psychological distress that can follow an hepatitis C virus diagnosis. Conclusion Our findings provide a compelling case for a proactive nursing response, which will improve women's experiences of hepatitis C virus diagnosis and, in turn, enhance women's access to hepatitis C virus care and other healthcare services.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".