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Record W4405729160 · doi:10.1177/20503245241308745

Who's asking me? Service user perspectives on safer injecting facilities

2024· article· en· W4405729160 on OpenAlexaboutno aff
Petra Salisbury, Darren Hill

Bibliographic record

VenueDrug Science Policy and Law · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsSAFERService (business)Internet privacyBusinessComputer scienceComputer securityMarketing

Abstract

fetched live from OpenAlex

This paper discusses the topic of safer injecting facilities with those who are likely to use them. Whilst more countries are adopting this harm reduction strategy, which is widely acknowledged for reducing drug-related death and injury, the UK have been resistant to their implementation. This qualitative discussion paper reflects on the overwhelming global evidence around safer injecting facilities whilst also capturing the voice of those likely to use such facilities. Although there have been several evaluations from recently opened sites in both Australia and Canada, this paper's sole purpose was to have input directly from injecting drug users to help inform local policy and develop local service provision. This paper recognises that the best people to contribute to policy are those who are directly affected by the matter itself. This small and localised piece of research interviewed nine injecting drug users and four drug workers. However, the focus of this paper is to capture the views of those injecting rather than those supporting them. By using thematic analysis, their responses were interpreted and enabled us to recognise three main themes identified by the service users themselves: yes to safe injecting facility, chaos in the injecting community and more than just a clinic. There were points where each of these themes also raised concerns, not only about their own welfare but also that of the wider public. The service users showed insight and sensitivity regarding such a facility but offered pragmatic suggestions that could help shape any possible provision.

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 score0.594
Threshold uncertainty score0.736

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.362
Teacher spread0.324 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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