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Record W4408147870 · doi:10.1097/cxa.0000000000000227

How Do We Move the Needle on Needle Debris? A Qualitative Interview Study With Reflexive Thematic Analysis, From SANDS (Strategies for Addressing Needle Debris Study)

2025· article· en· W4408147870 on OpenAlexafffundvenue
Jennifer Jackson, Emily Ainsley, Samantha Perry, Farida Gadimova, Twyla Ens, Tianna Cameron, Rafael Francisco, Yebin Kim, Emma McGill, Sukhdeep Sodhi, J. Yu, Carla Ginn

Bibliographic record

VenueThe Canadian Journal of Addiction · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Calgary
FundersO'Brien Institute for Public Health, University of Calgary
KeywordsDebrisThematic analysisReflexivityQualitative researchQualitative analysisGeologySociologySocial scienceOceanography

Abstract

fetched live from OpenAlex

ABSTRACT Objective: Needle debris refers to discarded drug paraphernalia that is associated with substance use and is a challenging issue for many municipalities. The presence of needle debris can decrease public support for harm reduction services, because of fears around public safety. We examined perceptions from people in both the public and private sectors in an urban municipality regarding needle debris prevention and management. Methods: We conducted semistructured interviews with 16 participants who manage needle debris cleanup for the local municipality, agencies, and businesses. The method for our analysis was reflexive thematic analysis using inductive coding. Results: Stigma was the main contextual factor in managing needle debris. Participants identified practical reasons why needle debris occurred, describing it as a social issue, and not a waste issue. Participants had varied preferences for centralized versus whole-of-society approaches to addressing needle debris. Proposed solutions included using less stigmatizing bin designs and creating a culture change around needle debris to create better services for people who use substances. Conclusions: Needle debris is complex and social considerations need to be part of any needle debris policy interventions. Contexte: Les débris d’aiguilles désignent les accessoires de consommation de drogue mis au rebut qui sont associés à la consommation de substances psychoactives et constituent un problème difficile pour de nombreuses municipalités. La présence de débris d’aiguilles peut diminuer le soutien du public aux services de réduction des risques, en raison des craintes liées à la sécurité publique. Nous avons examiné les perceptions des personnes des secteurs public et privé d’une municipalité urbaine concernant la prévention et la gestion des débris d’aiguilles. Méthodes: Nous avons mené des entretiens semi-structurés avec 16 participants, qui gèrent le nettoyage des débris de seringues pour la municipalité locale, les agences et les entreprises. Nous avons procédé à une analyse thématique réflexive à l’aide d’un codage inductif. Résultats: La stigmatisation est le principal facteur contextuel de la gestion des débris d’aiguilles. Les participants ont identifié des raisons pratiques pour lesquelles les débris d’aiguilles se produisaient, les décrivant comme un problème social et non comme un problème de déchets. Les participants avaient des préférences variées pour des approches centralisées ou pour des approches globales de la société pour traiter les débris d’aiguilles. Les solutions proposées comprennent l’utilisation de poubelles moins stigmatisantes et la création d’un changement culturel autour des débris d’aiguilles afin de créer de meilleurs services pour les personnes qui consomment des substances. Conclusions: La gestion des débris d’aiguilles est complexe et les considérations sociales doivent faire partie de toute intervention politique sur les débris d’aiguilles.

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)0.025
metaresearch head score (Gemma)0.026
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 score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0120.012
Scholarly communication0.0050.006
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.113
GPT teacher head0.401
Teacher spread0.288 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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