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Demon Rum: High-Tech Solutions to an Age-Old Problem

2005· article· en· W2048874832 on OpenAlexaboutno aff
Scott T. Walters, Reid K. Hester, Emil Chiauzzi, Elizabeth Miller

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2005
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionThe InternetIntervention (counseling)Web sitePsychologyMillerMedical educationMedicineWorld Wide WebComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

This article summarizes the proceedings of a symposium at the 2004 Research Society on Alcoholism Meeting in Vancouver, British Columbia, Canada, organized and chaired by Scott T. Walters. The purpose of the symposium was to describe several brief motivational interventions offered via the Internet, including the evidence for web-based interventions, applications and contexts in which such approaches are being used, and directions for future research. Walters provided an overview and introduction to the topic and discussed the e-CHUG (www.e-chug.com) and e-TOKE (www.e-toke.com) feedback interventions for college alcohol and marijuana prevention, including the contexts in which they are being used and ways they are being integrated with other campus prevention efforts. Dr. Hester presented 12-month results from a controlled trial of the Drinker's Check-up (www.drinkerscheckup.com), an intervention for adult problem drinkers that is available both as a Windows and as an Internet application. Dr. Chiauzzi described the development and testing of My Student Body (www.mystudentbody.com), a tailored drinking prevention web site for college students. Finally, Dr. Miller addressed the use of online assessment and feedback to reduce drinking, including the history of web-based interventions and their likely future and the potential limitations of such approaches.

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)0.001
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 score0.235
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.265
GPT teacher head0.506
Teacher spread0.241 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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