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Record W2139145512 · doi:10.1136/sextrans-2012-050775

Geographical and temporal variation of injection drug users in Pakistan

2013· article· en· W2139145512 on OpenAlexaff
Chris Archibald, Souradet Y. Shaw, Faran Emmanuel, Suleman Otho, Tahira Reza, Arshad Altaf, Nighat Musa, Laura H. Thompson, James Blanchard

Bibliographic record

VenueSexually Transmitted Infections · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsMedicineVariation (astronomy)DrugOptometryPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVES: We describe the characteristics of injecting drug users (IDU) in Pakistan in 2006 and 2011, and assess the heterogeneity of IDU characteristics across different cities and years as well as factors associated with HIV infection. METHODS: Cross-sectional, integrated behavioural-biological surveys of IDU were conducted in 10 cities across Pakistan in 2006 and 2011. Univariate and multivariable analyses were used to describe the differences in HIV prevalence and risk behaviours between cities and over time. RESULTS: Large increases in HIV prevalence among injection drug users in Pakistan were observed, with overall HIV prevalence increasing from 16.2% in 2006 to 31.0% in 2011; an increase in HIV prevalence was also seen in all geographic areas except one. There was an increase in risk behaviours between 2006 and 2011, anecdotally related to a reduction in the availability of services for IDU. In 2011, larger proportions of IDU reported injecting several times a day and using professional injectors, and fewer reported always using clean syringes. An increase in the proportion living on the street was also observed and this was associated with HIV infection. Cities differ in terms of HIV prevalence, risk profiles, and healthcare seeking behaviours. CONCLUSIONS: There is a high prevalence of HIV among injection drug users in Pakistan and considerable potential for further transmission through risk behaviours. HIV prevention programs may be improved through geographic targeting of services within a city and for involving groups that interact with IDU (such as pharmacy staff and professional injectors) in harm reduction initiatives.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.303
Teacher spread0.289 · 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 designObservational
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

Citations16
Published2013
Admission routes1
Has abstractyes

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