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Access to health and social services for IDU: The impact of a medically supervised injection facility

2009· article· en· W1569499198 on OpenAlexaffabout
Will Small, Natasha Van Borek, Nadia Fairbairn, Evan Wood, Thomas Kerr

Bibliographic record

VenueDrug and Alcohol Review · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
Fundersnot available
KeywordsThematic analysisInjection drug useHealth careMedicineQualitative researchSocial WelfareFamily medicineNursingPsychologyDrug injectionSociologyHuman immunodeficiency virus (HIV)Political science

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Injection drug users (IDU) often experience barriers to conventional health-care services, and consequently might rely on acute and emergency services. This study sought to investigate IDU perspectives regarding the impact of supervised injection facility (SIF) use on access to health-care services. DESIGN AND METHODS: Semi-structured qualitative interviews were conducted with 50 Vancouver-based IDU participating in the Scientific Evaluation of Supervised Injecting cohort. Audio-recorded interviews elicited IDU perspectives regarding the impact of SIF use on access to health and social services. Interviews were transcribed verbatim and a thematic analysis was conducted. RESULTS: Fifty IDU, including 21 women, participated in this study. IDU narratives indicate that the SIF serves to facilitate access to health care by providing much-needed care on-site and connects IDU to external services through referrals. Participants' perspectives suggest that the SIF has facilitated increased uptake of health and social services among IDU. DISCUSSION AND CONCLUSIONS: Although challenges related to access to care remain in many settings, SIF have potential to promote health by facilitating enhanced access to health-care and social services through a model of care that is accessible to high-risk IDU.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.093
GPT teacher head0.448
Teacher spread0.356 · 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 designOther design
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

Citations50
Published2009
Admission routes2
Has abstractyes

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