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Record W2968822399 · doi:10.1177/2167696819867533

Emerging Adults’ Public and Private Discussions of Substance Use on Social Media

2019· article· en· W2968822399 on OpenAlexaboutno aff
Madeleine J. George, Samuel E. Ehrenreich, Kaitlyn Burnell, Allycen R. Kurup, Justin W. Vollet, Marion K. Underwood

Bibliographic record

VenueEmerging Adulthood · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSubstance useQuarter (Canadian coin)Social mediaPsychologySample (material)Social psychologyClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

Substance use increases during emerging adulthood and may relate to higher concurrent and future problems. For a community sample of 140 emerging adults, this study explores the associations between reported alcohol, marijuana, and tobacco use in 12th grade, the content of public posts and private messaging on Facebook during the fall after 12th grade, and self-reported substance use 1 year after high school. About one quarter of participants discussed substances publicly, and nearly half discussed substances privately on Facebook as observationally coded by researchers. Twelfth-grade substance use predicted the probability of engaging in public and private substance-related discussions. Tobacco and marijuana use predicted the frequency of private messaging about substances. Public and private online substance discussions predicted positive changes in marijuana use 1 year later. Results from this study suggest that social media discussions about substances, particularly private messages, may signal and shape emerging adults’ substance use behaviors.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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