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Record W2575635822

Profile of People Who Inject Drugs in Tehran, Iran.

2016· article· en· W2575635822 on OpenAlexaff
Masoumeh Amin‐Esmaeili, Afarin Rahimi‐Movaghar, Maryam Gholamrezaei, Emran Mohammad Razaghi

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineHarm reductionHeroinOpiumDrugSyringeDrug userSubstance abuseDrug injectionPublic healthEnvironmental healthPsychiatryFamily medicineTraditional medicineHuman immunodeficiency virus (HIV)Nursing
DOInot available

Abstract

fetched live from OpenAlex

The marked shift in the patterns of drug use in Iran, from opium smoking to injecting drug use, has led to serious health-related outcomes. This study was designed to explore characteristics of people who inject drugs (PWID) in Tehran, Iran. Nine hundred and four PWID were recruited from treatment and harm reduction facilities, as well as drug user hangouts in public areas in Tehran. Participants were interviewed using the Persian version of the World Health Organization Drug Injecting Study Phase II questionnaire. The median age at the time of the first illegal drug use, at the time of the first injection and current age was 20, 24 and 32, respectively. In more than 80% of the cases, the first drug used was opium. The transition from the first drug use to the first drug injection occurred after an average of 6.6 and 2.7 years for those who had started drug use with opium and heroin, respectively. Two-thirds of the participants shared injecting equipment within the last 6 months. Difficulty in obtaining sterile needles and thehigh cost of syringes were reported as the major reasons for needle/syringe sharing. Approximately 80% of community-recruited PWID reported difficulties in using treatment or harm reduction services. Self-detoxification and forced detoxification were the most common types of drug abuse treatment in alifetime. Despite a dramatic shift in drug policy in Iran during the past few years, wider coverage of harm reduction services, improvement of the quality of services, and education about such services are still necessary.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.035
GPT teacher head0.289
Teacher spread0.254 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

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