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Record W2806826515 · doi:10.1080/07448481.2018.1462821

Efficacy of interventions targeting alcohol, drug and smoking behaviors in university and college students: A review of randomized controlled trials

2018· review· en· W2806826515 on OpenAlexaff
Ronald C. Plotnikoff, Sarah A. Costigan, Sarah G. Kennedy, Sara L. Robards, John Germov, C. Wild

Bibliographic record

VenueJournal of American College Health · 2018
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionCollege healthMedicineRandomized controlled trialDrugAlcohol consumptionAlcoholEnvironmental healthGerontologyFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effectiveness of interventions targeting alcohol consumption, drug use and smoking for college/university students. PARTICIPANTS: College/University students. METHODS: Studies were eligible if: (1)included students attending universities/colleges; (2)implemented in a university/college setting; (3)aimed to improve at least one of the following behaviors: alcohol and/or drug use and/or smoking; (4)were RCTs. The effect of the interventions on behaviors was determined by the percentage of studies that reported an effect. Due to the heterogeneity of outcomes meta-analysis was not conducted. RESULTS: 88 studies met criteria. University-based interventions were effective for reducing alcohol-related outcomes (drinking patterns, BAC, consequences, problem drinking). Inconsistent findings for drug and smoking were observed. CONCLUSIONS: University-based interventions have the potential to improve health for students. While there is a breadth of research examining the efficacy of interventions to reduce alcohol consumption, further research is needed to determine the best approach for addressing smoking and drug use among students.

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)0.017
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.189
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0230.003
Bibliometrics0.0010.001
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 head0.072
GPT teacher head0.438
Teacher spread0.366 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations33
Published2018
Admission routes1
Has abstractyes

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