MétaCan
Menu
Back to cohort
Record W2150280138 · doi:10.1186/1477-7517-7-5

Methamphetamine use and malnutrition among street-involved youth

2010· article· en· W2150280138 on OpenAlexafffundabout
Dan Werb, Thomas Kerr, Ruth Zhang, Julio Montaner, Evan Wood

Bibliographic record

VenueHarm Reduction Journal · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BC
KeywordsMalnutritionEnvironmental healthMedicineOdds ratioMultivariate analysisCohortCohort studyProspective cohort studyDemographyGerontologyInternal medicineSociology

Abstract

fetched live from OpenAlex

We sought to explore the effect of crystal methamphetamine use on the risk of experiencing malnutrition among street-involved youth in Vancouver, Canada. Risk of malnutrition was defined as being hungry but not having enough money to buy food. Socio-demographic and drug use factors associated with risk of malnutrition were investigated using univariate and multivariate analysis among a prospective cohort of street-involved youth known as the At-Risk Youth Study (ARYS). Between September 2005 and December 2006, 509 street-involved youth were enrolled in ARYS, among whom 21% reported being at risk of malnutrition as defined above in the previous six months. In multivariate analysis, only non-injection crystal methamphetamine was significantly associated with being at risk of malnutrition among this cohort (Adjusted Odds Ratio [AOR] = 1.60, 95% Confidence Interval [CI]: 1.03-2.48, p = 0.036). Interventions seeking to address food insecurity among street youth may benefit from considering drug use patterns since methamphetamine use predicted higher risk in this setting.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.089
GPT teacher head0.383
Teacher spread0.294 · 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
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

Citations34
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueHarm Reduction JournalSame topicHomelessness and Social IssuesFrench-language works237,207