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Record W2093857825 · doi:10.1186/1477-7517-7-11

Impact: a case study examining the closure of a large urban fixed site needle exchange in Canada

2010· article· en· W2093857825 on OpenAlexaffabout
Joan MacNeil, Bernie Pauly

Bibliographic record

VenueHarm Reduction Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarm reductionClosure (psychology)Context (archaeology)Public healthHarmBusinessMedicineGeographyPolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

INTRODUCTION: In 2008, one of the oldest fixed site needle exchanges in a large urban city in Canada was closed due to community pressure. This service had been in existence for over 20 years. CASE DESCRIPTION: This case study focuses on the consequences of the switch to mobile needle exchange services immediately after the closure and examines the impact of the closure on changes in risk behavior related to drug use, needle distribution and access to services The context surrounding the closure was also examined. DISCUSSION AND EVALUATION: After the closure of the fixed site exchange, access to needle exchange services decreased as evidenced by the sharp decline in numbers of clients reached, and the numbers of needles distributed and collected monthly. Reports related to needle reuse and selling of syringes suggest changes in risk behaviors. Thousands of needles remain unaccounted for in the community. To date, a new fixed site has not been found. CONCLUSION: Closing the fixed site needle exchange had an adverse effect on already vulnerable clients and reduced access to comprehensive harm reduction services. While official public policy supports a fixed site, politicization of the issue has meant a significant setback for harm reduction with reduced potential to meet public health targets related to reducing the spread of blood borne diseases. This situation is unacceptable from a public health perspective.

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 score0.387
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.346
Teacher spread0.298 · 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

Citations37
Published2010
Admission routes2
Has abstractyes

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