“With a PICC line, you never miss”: The role of peripherally inserted central catheters in hospital care for people living with HIV/HCV who use drugs
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD), and especially those who inject drugs, are at increased risk of acquiring bloodborne infections (e.g., HIV and HCV), experiencing drug-related harms (e.g., abscesses and overdose), and being hospitalized and requiring inpatient parenteral antibiotic therapy delivered through a peripherally inserted central catheter (PICC). The use of PICC lines with PWUD is understood to be a source of tension in hospital settings but has not been well researched. Drawing on theoretical and analytic insights from "new materialism," we consider the assemblage of sociomaterial elements that inform the use of PICCs. METHODS: This paper draws on n = 50 interviews conducted across two related qualitative research projects within a program of research about the impact of substance use on hospital admissions from the perspective of healthcare providers (HCPs) and people living with HIV/HCV who use drugs. This paper focuses on data about PICC lines collected in both studies. RESULTS: The decision to provide, maintain, or remove a PICC is based on a complex assemblage of factors (e.g., infections, bodies, drugs, memories, relations, spaces, temporalities, and contingencies) beyond whether parenteral intravenous antibiotic therapy is clinically indicated. HCPs expressed concerns about the risk posed by past, current, and future drug use, and contact with non-clinical spaces (e.g., patient's homes and the surrounding community), with some opting for second-line treatments and removing PICCs. The majority of PWUD described being subjected to threats of discharge and increased monitoring despite being too ill to use their PICC lines during past hospital admissions. A subset of PWUD reported using their PICC lines to inject drugs as a harm reduction strategy, and a subset of HCPs reported providing harm reduction-centred care. CONCLUSION: Our analysis has implications for theorizing the role of PICC lines in the care of PWUD and identifies practical guidance for engaging them in productive and non-judgemental discussions about the risks of injecting into a PICC line, how to do it safely, and about medically supported alternatives.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| 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".