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Record W4296117074 · doi:10.1177/00220426221123269

Improving Harm Reduction Services: A Qualitative Study on the Perspectives of Highly Marginalized Persons Who Inject Drugs in Montreal

2022· article· en· W4296117074 on OpenAlexaffabout
Hélène Poliquin, Michel Perreault, Ana Cecilia Villela Guilhon, Karine Bertrand

Bibliographic record

VenueJournal of Drug Issues · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversité de SherbrookeMcGill UniversityInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsHarm reductionPsychosocialFocus groupHarmPublic relationsExperiential learningQualitative researchSocial workMedicinePsychologySociologyMedical educationNursingPublic healthSocial psychologyBusinessPolitical sciencePsychiatryMarketingPedagogy

Abstract

fetched live from OpenAlex

Harm reduction (HR) is an alternative to the moralization of drug use and a pragmatic public health approach aimed at minimizing harms associated with use. This study sought to gain the perspectives of persons who inject drugs (PWID) on the adequacy of services provided by HR organizations in Montreal. Twenty-two semi-structured interviews and two focus groups were conducted with 30 participants. Some of the key advantages of HR perceived by participants include access to injection equipment, psychosocial support, and reduced social isolation. However, many wanted more opportunities for social insertion and greater value to be placed on their knowledge and life experiences (e.g., experiential knowledge of the street scene, drug use, sex work, or homelessness). This study suggests that PWID who access HR services in Montreal are interested in paid work opportunities in environments that promote power sharing, and activities that are conducted and managed by and for them.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.541
Threshold uncertainty score0.912

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.008
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.374
Teacher spread0.339 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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