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Record W2153581859 · doi:10.2196/mental.3278

A Web-Based Intervention for Users of Amphetamine-Type Stimulants: 3-Month Outcomes of a Randomized Controlled Trial

2014· article· en· W2153581859 on OpenAlexvenueno aff
Robert J. Tait, Rebecca McKetin, Frances Kay‐Lambkin, Bradley Carron‐Arthur, Anthony Bennett, Kylie Bennett, Helen Christensen, Kathleen M Griffiths

Bibliographic record

VenueJMIR Mental Health · 2014
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilCurtin University of Technology
KeywordsRandomized controlled trialMedicineIntervention (counseling)Clinical psychologyPsychological interventionCannabisAcceptance and commitment therapyCognitive behavioral therapyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Among illicit drugs, the prevalence of amphetamine-type stimulant (ATS) use is second only to cannabis. Currently, there are no approved pharmacotherapies for ATS problems, but some face-to-face psychotherapies are effective. Web-based interventions have proven to be effective for some substance use problems, but none has specifically targeted ATS users. OBJECTIVE: The objective of the study was to evaluate the effectiveness of a Web-based intervention for ATS problems on a free-to-access site compared with a waitlist control group. METHODS: We used a randomized controlled trial design. The primary outcome measure was self-reported ATS use in the past three months assessed using the Alcohol, Smoking, Substance Involvement Screening Test (ASSIST). Other measures included quality of life (EUROHIS score), psychological distress (K-10 score), days out of role, poly-drug use, general help-seeking intentions, actual help-seeking, and "readiness to change". The intervention consisted of three fully automated, self-guided modules based on cognitive behavioral therapy and motivation enhancement. The analysis was an intention-to-treat analysis using generalized estimating equation models, with a group by time interaction as the critical assessment. RESULTS: We randomized 160 people (intervention n=81, control n=79). At three months, 35/81 (43%) intervention and 45/79 (57%) control participants provided follow-up data. In the intervention group, 51/81 (63%) completed at least one module. The only significant group by time interaction was for days out of role. The pre/post change effect sizes showed small changes (range d=0.14 to 0.40) favoring the intervention group for poly-drug use, distress, actual help-seeking, and days out of role. In contrast, the control group was favored by reductions in ATS use, improvements in quality of life, and increases in help-seeking intentions (range d=0.09 to 0.16). CONCLUSIONS: This Web-based intervention for ATS use produced few significant changes in outcome measures. There were moderate, but nonsignificant reductions in poly-drug use, distress, days partially out of role, and increases in help-seeking. However, high levels of participant attrition, plus low levels of engagement with the modules, preclude firm conclusions being drawn on the efficacy of the intervention and emphasize the problems of engaging this group of clients in a fully automated program. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry: ACTRN 12611000947909; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12611000947909 (Archived by WebCite at http://www.webcitation.org/6SHTxEnzP).

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.022
GPT teacher head0.360
Teacher spread0.338 · 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 designRandomized trial
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

Citations39
Published2014
Admission routes1
Has abstractyes

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