MétaCan
Menu
← Back to cohort
Record W6904822775 · doi:10.14288/1.0436946

Social and structural contexts of injectable opioid agonist treatment : a critical ethnographic study of people's experiences in Vancouver, BC

2024· article· en· W6904822775 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)EthnographyVulnerability (computing)Social vulnerabilityParticipant observationNeighbourhood (mathematics)Social environmentDowntownPsychological interventionGrounded theory

Abstract

fetched live from OpenAlex

Background: As part of the response to Canada’s worsening overdose crisis driven by a toxic adulterated drug supply, there has been increased attention to and expansion of drug treatment options, including injectable opioid agonist treatment (iOAT). Many people who access iOAT experience significant social inequities (insecure housing, poverty) and are impacted by multiple and intersecting forms of discrimination (racism, sexism). As this treatment has only recently expanded, there is a gap in research on how iOAT can help alleviate and account for these social inequities in the context of the ongoing adulteration of the illicit drug supply. This dissertation addresses this gap by examining how social and structural factors impact people’s engagement with iOAT to optimize program delivery. Methods: This dissertation draws on critical ethnographic and community-based approaches conducted with people accessing four iOAT programs in Vancouver’s Downtown Eastside neighbourhood from May 2018 to November 2019. Data included in-depth baseline and follow-up interviews and approximately 50 hours of observation fieldwork conducted in one iOAT program and with a subsample of participants in the surrounding neighbourhood. Analysis leveraged a structural vulnerability lens and complementing critical social science theories to characterize the social-structural dynamics that shape people’s engagement with iOAT. Results: This dissertation characterizes why people engage with iOAT and how they navigate its enabling and constraining aspects. Participants accessed iOAT for resources to address social inequities and physical harms (withdrawal, chronic pain, overdose) experienced in the context of the ongoing toxic drug supply. While iOAT provides access to life-saving medication and health and social supports, program operations (e.g., surveillance) can create barriers to engagement. Findings highlight how intersecting structural vulnerabilities, including gendered power dynamics, housing insecurity, poverty, inadequate chronic pain management, and drug use-related stigma, mediate how people engage with iOAT. Conclusion: Drawing attention to structural vulnerability, study findings highlight the diverse and pragmatic ways people engage with iOAT to achieve personal goals and manage their opioid use in a broader socio-political context and worsening overdose crisis. Findings point to the importance and modification of integrating low-threshold, equity-oriented, and patient-centred approaches to iOAT care to meet people’s diverse needs.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.184
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0240.010
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.290
Teacher spread0.266 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)→Same topicHIV, Drug Use, Sexual Risk→French-language works237,207→