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Record W3127305550 · doi:10.1186/s12939-021-01420-7

“I want to get better, but…”: identifying the perceptions and experiences of people who inject drugs with respect to evolving hepatitis C virus treatments

2021· article· en· W3127305550 on OpenAlexafffundabout
Trevor Goodyear, Helen Brown, Annette J. Browne, Peter Hoong, Lianping Ti, Rod Knight

Bibliographic record

VenueInternational Journal for Equity in Health · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of British Columbia HospitalBritish Columbia Centre on Substance UseUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institute on Drug AbuseUniversity of British ColumbiaMichael Smith Health Research BCCanadian Nurses Foundation
KeywordsMedicineHepatitis CPopulationFamily medicineHealth careHealth administrationHealth services researchPublic healthGatekeepingHepatitis C virusNursingInternal medicineEnvironmental healthVirologyVirus

Abstract

fetched live from OpenAlex

BACKGROUND: The advent of highly tolerable and efficacious direct-acting antiviral (DAA) medications has transformed the hepatitis C virus (HCV) treatment landscape. Yet, people who inject drugs (PWID) - a population with inequitably high rates of HCV and who face significant socio-structural barriers to healthcare access - continue to have disproportionately low rates of DAA uptake. The objective of this study is to explore how PWID with lived experience of HCV perceive and experience DAA treatment, in a setting with universal coverage of these medications since 2018. METHODS: Informed by a critical interpretive framework, we thematically analyze data from in-depth, semi-structured interviews conducted between January and June 2018 in Vancouver, Canada, with a purposive sample (n = 56) of PWID at various stages (e.g., pre, peri, post) of DAA treatment. RESULTS: The analysis yielded three key themes: (i) life with HCV, (ii) experiences with and perceptions of evolving HCV treatments, and (iii) substance use and the uptake of DAA treatments. First, participants described how health and healthcare conditions, such as the deprioritizing of HCV (e.g., due to: being asymptomatic, healthcare provider inaction, gatekeeping) and catalysts to care (e.g., symptom onset, treatment for co-morbidities) shaped DAA treatment motivation and access. Second, participants described how individual and community-level accounts of evolving HCV treatments, including skepticism following negative experiences with Interferon-based treatment and uncertainty regarding treatment eligibility, negatively influenced willingness and opportunities to access DAAs. Concurrently, participants described how peer and community endorsement of DAAs was positively associated with treatment uptake. Third, participants favoured HCV care that was grounded in harm reduction, which included the integration of DAAs with other substance use-related services (e.g., opioid agonist therapy, HIV care), and which was often contrasted against abstinence-focused care wherein substance use is framed as a contraindication to HCV treatment access. CONCLUSIONS: These findings underscore several equity-oriented healthcare service delivery and clinician adaptations that are required to scale up DAAs among PWID living with HCV, including the provision of harm reduction-focused, non-stigmatizing, integrated, and peer-led care that responds to power differentials.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.013
Scholarly communication0.0060.006
Open science0.0020.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.089
GPT teacher head0.475
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2021
Admission routes3
Has abstractyes

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