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Record W2010094622 · doi:10.1081/ja-120017619

Towards More Effective Public Health Programming for Injection Drug Users: Development and Evaluation of the Injection Drug User Quality of Life Scale

2003· article· en· W2010094622 on OpenAlexafffundabout
Susan B. Brogly, Céline Mercier, Julie Bruneau, Anita Palepu, Eduardo L. Franco

Bibliographic record

VenueSubstance Use & Misuse · 2003
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCentre for Health Evaluation and Outcome SciencesSt. Paul's HospitalCentre Hospitalier de l’Université de MontréalMcGill University
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchHealth Canada
KeywordsConcordanceScale (ratio)Quality of life (healthcare)Psychological interventionReliability (semiconductor)PsychometricsMedicinePublic healthDrugGerontologyPsychologyPsychiatryClinical psychologyNursing

Abstract

fetched live from OpenAlex

The psychometric properties of the Injection Drug User Quality of Life Scale (IDUQOL) were assessed in 61 Montreal IDUs in 2001, 85% of whom were reinterviewed within four weeks. The reliability of the IDUQOL was acceptable (ICC = 0.71) and concordance between the IDUQOL and the Flanagan Quality of Life Scale was moderate (Pearson coefficient = 0.57). Quality of life was negatively associated with injection cocaine and emergency department use with both instruments; these results were more striking for the IDUQOL. The IDUQOL is a culturally relevant quality of life instrument with good psychometric properties. The IDUQOL may be useful in the development and evaluation of interventions for IDUs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.120
GPT teacher head0.400
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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

Citations56
Published2003
Admission routes3
Has abstractyes

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