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Record W2807581623 · doi:10.1186/s13011-018-0159-0

Financial hardship and drug use among men who have sex with men

2018· article· en· W2807581623 on OpenAlexfundno aff
Su Hyun Park, Yazan A. Al‐Ajlouni, Joseph J. Palamar, William C. Goedel, Anthony Estreet, Brian Elbel, Scott E. Sherman, Dustin T. Duncan

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseCenters for Disease Control and PreventionSchool of Medicine, New York UniversityNational Institutes of HealthNational Institute of Mental HealthYork University
KeywordsDrugPsychologyFinanceBusinessPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about the role of financial hardship as it relates to drug use, especially among men who have sex with men (MSM). As such, this study aimed to investigate potential associations between financial hardship status and drug use among MSM. METHODS: We conducted a cross-sectional survey of 580 MSM in Paris recruited using a popular geosocial-networking smartphone application (GSN apps). Descriptive analyses and multivariate analyses were performed. A modified Poisson model was used to assess associations between financial hardship status and use of drugs (any drugs, tobacco, alcohol, marijuana, inhalant nitrites, and club drugs). RESULTS: In our sample, 45.5% reported that it was somewhat, very, or extremely difficult to meet monthly payments of bills (high financial hardship). In multivariate analyses, a high level of financial hardship was significantly associated with an increased likelihood of reporting use of any substance use (adjusted risk ratio [aRR] = 1.15; 95% CI = 1.05-1.27), as well as use of tobacco (aRR = 1.45; 95% CI = 1.19-1.78), marijuana (aRR = 1.48; 95% CI =1.03-2.13), and inhalant nitrites (aRR = 1.24; 95% CI = 1.03-1.50). CONCLUSIONS: Financial hardship was associated with drug use among MSM, suggesting the need for interventions to reduce the burden of financial hardship in this population.

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 score0.377
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.330
Teacher spread0.305 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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