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Record W7046582803

Effective prevention of adolescent substance abuse - Educational versus deterrent approaches

2012· article· en· W7046582803 on OpenAlexaboutno aff

Bibliographic record

VenueThe HKU Scholars Hub (University of Hong Kong) · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialSubstance abuseSubstance abuse preventionSubstance useDrug educationEmpirical research
DOInot available

Abstract

fetched live from OpenAlex

Substance abuse, especially among adolescents, has long been an important issue in society. In light of the adverse impact of substance abuse, scholars, educators, and policy-makers have proposed different approaches to prevent and reduce such abuse. This paper investigates the effectiveness of the two prominent approaches-educational and deterrent-in preventing and reducing adolescent substance abuse. The educational approach (e.g., school-based prevention programming) tends to be more comprehensive and better grounded in theories than the deterrent approach (e.g., drug testing). The educational approach not only targets multiple psychosocial factors contributing to substance abuse, but it is also supported by empirical studies showing that school-based prevention programming is effective in preventing substance abuse and has long-lasting positive influences on adolescent development. Practical implications of implementing school-based prevention programming are also discussed. © 2012 The Governors of the University of Alberta.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.256
Teacher spread0.215 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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