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Record W2114001730 · doi:10.1080/14639230600562816

Access to the Internet among drinkers, smokers and illicit drug users: Is it a barrier to the provision of interventions on the World Wide Web?

2006· article· en· W2114001730 on OpenAlexaffabout
John Cunningham, Peter Selby, Kypros Kypri, Keith Humphreys

Bibliographic record

VenueMedical Informatics and the Internet in Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsThe InternetCannabisPsychological interventionIllicit drugPopulationInternet accessPsychiatryInternet privacyMedicineAdvertisingBusinessEnvironmental healthDrugWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Expanding Internet-based interventions for substance use will have little benefit if heavy substance users are unlikely to have Internet access. This paper explored whether access to the Internet was a potential barrier to the provision of services for smokers, drinkers and illicit drug users. METHODS: As part of a general population telephone survey of adults in Ontario, Canada, respondents were asked about their use of different drugs and also about their use of the Internet. RESULTS: Pack-a-day smokers were less likely (48%) to have home Internet access than non-smokers (69%), and current drinkers (73%) were more likely to have home access than abstainers (50%). These relationships remained true even after controlling for demographic characteristics. Internet access was less clearly associated with cannabis or cocaine use. CONCLUSIONS: Even though there is variation in access among smokers, drinkers and illicit drug users, the World Wide Web remains an excellent opportunity to potentially provide services for substance abusers who might never access treatment in person because, in absolute terms, the majority of substance abusers do use the Internet.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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 designNot applicable
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

Citations52
Published2006
Admission routes2
Has abstractyes

Explore more

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