Integrated Models of Care for People Living with Hepatitis C Virus and a Substance Use Disorder: Protocol for a Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND People living with a substance use disorder (SUD) are a key population within the hepatitis C virus (HCV) epidemic. While integrated and community-based models of care have shown positive outcomes among this population, the literature has been primarily focused on the HIV context. This paper outlines a systematic review protocol on the impact of various integrated models of care, which includes HCV and SUD services, on various treatment, and health-related outcomes among this population. OBJECTIVE The objective of this review is to determine the impact of integrated models of care on HCV and addiction treatment and health-related outcomes for adults living with HCV and an SUD. METHODS We will search 5 databases, article reference lists, and abstracts from relevant conferences that investigate the impact of integrated models of care on treatment and health-related outcomes among people living with HCV and an SUD. Database searches will be conducted and titles, abstracts, and the full-text of the relevant studies will be independently reviewed in separate stages. The methodological quality of included studies will be assessed using a validated tool. Data from included articles will be extracted using a standardized form and synthesized in a narrative account. RESULTS For this project, we have received funding from the Canadian Institute of Health Research. To date, we have completed the search strategy, reviewed the titles, abstracts, and full-texts. Grading the selected studies and qualitative synthesis of the results are currently under way, and we expect the final results to be submitted for publication in the fall of 2018. CONCLUSIONS The systematic review will describe different integrated models of care that could be effective in improving the health and well-being of people living with HCV and an SUD. Results of this review could also identify quality improvement strategies to minimize the health and cost burden imposed on patients, healthcare professionals, and the healthcare system. CLINICALTRIAL PROSPERO CRD42017078445; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=78445 (Archived by WebCite at http://www.webcitation.org/6z4YnkE9G)
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 | 0.088 | 0.131 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.013 |
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".