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Record W2890580970 · doi:10.1080/09687637.2018.1497145

Looking beyond harm: meaning and purpose of substance use in the lives of marginalized people who use drugs

2018· article· en· W2890580970 on OpenAlexafffundabout
Andrew Ivsins, Kevin Yake

Bibliographic record

VenueDrugs Education Prevention and Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsHarmQualitative researchMeaning (existential)Substance useNarrativeHarm reductionMental healthDrugPsychologyEmbodied cognitionMedicinePsychiatrySocial psychologySociologyPsychotherapistPublic healthSocial scienceNursing

Abstract

fetched live from OpenAlex

Substance use among marginalized populations has historically been constructed as a social problem to be managed, cured, and eliminated. Much social science research concerning drug use among marginalized populations focuses on risks and harms, with little attention to positive aspects of substance use. In this paper we explore positive roles of drugs/drug use among marginalized people who use drugs. We draw on in-depth qualitative interviews conducted with 50 people who use drugs in Vancouver’s Downtown Eastside neighbourhood. Forty-three participants reported positive aspects of drug use. Participant narratives revealed four main themes regarding the role and function of drugs and drug use in their lives: (1) pain relief and management; (2) alleviating mental health issues; (3) fostering social experiences; (4) pleasurable embodied experiences. Our findings show that despite known negative consequences of substance use, in many ways drug use was beneficial for these individuals. Our study demonstrates that given the opportunity, meaningful and useful conversations that shed light on why people take drugs is possible. By understanding why marginalized individuals choose to consume the drugs they do we can begin to engage in truly helpful conversations about how to reduce drug-related harm.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.364
Teacher spread0.326 · 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 teacher head, 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

Citations20
Published2018
Admission routes3
Has abstractyes

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