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Record W3150076300 · doi:10.1080/07448481.2021.1900196

Substance use and victimization experiences among college students in Spain

2021· article· en· W3150076300 on OpenAlexaff
Francisco Caravaca, Eva Aizpurúa, Lindsay A. Taliaferro, Andrew L. Stephenson

Bibliographic record

VenueJournal of American College Health · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsTrinity College
Fundersnot available
KeywordsPolysubstance dependencePsychologyIntervention (counseling)Substance useBivariate analysisSubstance abuseClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined relationships between different types and patterns of substance use and victimization experiences. PARTICIPANTS: College students from two universities in Spain completed an online, cross-sectional survey. METHOD: A series of tetrachoric correlations were estimated to examine the bivariate associations between different types of substances and victimization experiences. Regression analyses then examined relationships between patterns of substance use (non-users, mono-users, and poly-users), and victimization. RESULTS: Slightly over half (57.7%) of students reported any form of victimization during the previous year, and most students (87.0%) reported using substances during the last three months. Polysubstance users were more likely than non-users to experience any victimization, as well as multiple forms of victimization. CONCLUSIONS: Findings highlight the importance of differentiating types and patterns of substance use when developing prevention and intervention programs. Implementing intervention strategies that target polysubstance users and decrease substance use could reduce rates of victimization among college 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)0.001
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 score0.079
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.333
Teacher spread0.311 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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