A Descriptive Model of Patient Readiness, Motivators, and Hepatitis C Treatment Uptake among Australian Prisoners
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus infection (HCV) has a significant global health burden with an estimated 2%-3% of the world's population infected, and more than 350,000 dying annually from HCV-related conditions including liver failure and liver cancer. Prisons potentially offer a relatively stable environment in which to commence treatment as they usually provide good access to health care providers, and are organised around routine and structure. Uptake of treatment of HCV, however, remains low in the community and in prisons. In this study, we explored factors affecting treatment uptake inside prisons and hypothesised that prisoners have unique issues influencing HCV treatment uptake as a consequence of their incarceration which are not experienced in other populations. METHOD AND FINDINGS: We undertook a qualitative study exploring prisoners' accounts of why they refused, deferred, delayed or discontinued HCV treatment in prison. Between 2010 and 2013, 116 Australian inmates were interviewed from prisons in New South Wales, Queensland, and Western Australia. Prisoners experienced many factors similar to those which influence treatment uptake of those living with HCV infection in the community. Incarceration, however, provides different circumstances of how these factors are experienced which need to be better understood if the number of prisoners receiving treatment is to be increased. We developed a descriptive model of patient readiness and motivators for HCV treatment inside prisons and discussed how we can improve treatment uptake among prisoners. CONCLUSION: This study identified a broad and unique range of challenges to treatment of HCV in prison. Some of these are likely to be diminished by improving treatment options and improved models of health care delivery. Other barriers relate to inmate understanding of their illness and stigmatisation by other inmates and custodial staff and generally appear less amenable to change although there is potential for peer-based education to address lack of knowledge and stigma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".