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Record W4413988849 · doi:10.1080/10826084.2025.2546498

Pubertal Development and the Onset of Substance Use Among Appalachian Youth: A Longitudinal Study

2025· article· en· W4413988849 on OpenAlexaff
Francesca G. De Geronimo, Christa L. Lilly, Steven M. Kogan, Jeanne Brooks‐Gunn, Álfgeir L. Kristjánsson, John P. Allegrante

Bibliographic record

VenueSubstance Use & Misuse · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsColumbia College
Fundersnot available
KeywordsSubstance usePsychologyLongitudinal studyAppalachiaAdolescent developmentDevelopmental psychologyAppalachian RegionClinical psychologyPsychiatryDemographyMedicineGerontologyGeographySociology

Abstract

fetched live from OpenAlex

Objective: We investigated the links between pubertal timing and tempo and the onset of cannabis, alcohol, and tobacco use among middle schoolers in Appalachian communities. Methods: School surveys were administered to middle school students of the 6th grade and continuing through the spring of the 8th grade (n = 2,587; 49.4% boys), beginning in the fall at six-month intervals. Youth self-reported on their pubertal development and substance use. We examined the influence of baseline levels and rate of growth in puberty as predictors of substance use onset with Latent Growth Curve Modeling (LGCM), stratified by sex/gender. Results: Our findings show that early pubertal timing was associated with an increased likelihood for onset of cannabis and alcohol use for boys, but not girls; pubertal tempo was not associated with onset. Conclusion: Early pubertal development may be a risk factor for early-onset substance use for boys. Prevention programming beginning in the first year of middle school is recommended.

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)0.000
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 score0.047
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.050
GPT teacher head0.291
Teacher spread0.242 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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