Associations between Adverse Childhood Experiences and Performance-Enhancing Substance Use among Young Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Adverse childhood experiences (ACEs) are associated with negative health outcomes, yet their associations with performance-enhancing substance (PES) use are unclear. This study aimed to determine whether ACEs predict greater use of legal and illegal PES in young adults. METHODS: = 14,322), Waves I (1994-1995) and III (2001-2002). ACEs included childhood sexual abuse, physical abuse, two neglect indicators, and cumulative ACEs. Legal (e.g. creatine monohydrate) and illegal (e.g. non-prescription anabolic-androgenic steroids; AAS) PES use was assessed. RESULTS: Sexual abuse had the greatest effect and predicted higher odds of legal PES use (men: adjusted odds ratio [AOR] 1.66, 95% confidence interval [CI] 1.06-2.59; women: AOR 3.74, 95% CI 1.63-8.59) and AAS use (men: AOR 8.89, 95% CI 5.37-14.72; women: AOR 5.73, 95% CI 2.31-14.18). Among men, a history of physical abuse (AOR 3.04, 95% CI 2.05-4.52), being left alone by a parent/guardian (AOR 2.33, 95% CI 1.50-3.60), and basic needs not being met (AOR 3.47, 95% CI 2.30-5.23) predicted higher odds of AAS use. Among women, basic needs not being met (AOR 2.94, 95% CI 1.43-6.04) predicted higher odds of AAS use. Among both men and women, greater number of cumulative ACEs predicted higher odds of both legal and illegal PES use. CONCLUSIONS: ACEs predict greater PES use among young adults. Clinicians should monitor for PES use among those who have experienced ACEs and provide psychoeducation on the adverse effects associated with PES use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".