The association between nonmedical use of prescription drugs and extreme weight control behavior among adolescents.
Bibliographic record
Abstract
Although extreme weight control behavior (EWCB) is associated with substance use, no research has examined the association between the nonmedical use of prescription drugs (NMUPD) and EWCB. Self-report data were collected from a sample of 4,148 students in Grades 9-12 enrolled in 5 high schools across the United States. Logistic regression models were constructed to examine the nonmedical use of prescription pain relievers, depressants, stimulants, and a composite measure for any NMUPD, and the EWCB of fasting, use of diet pills, powders, or liquids, and vomiting or laxative use. Models were estimated before and after controlling for key covariates for males and females. Approximately 16% of respondents reported any EWCB during the past 30 days, while 11% reported any NMUPD during the past 30 days. After covariate adjustment, any NMUPD was associated with any EWCB in both males and females (p < .05), and all EWCB remained significant in females who reported prescription pain reliever use (p < .01), with 2 out of 3 remaining significant for prescription stimulant and depressant use (p < .01). The only significant association detected for males was between prescription pain reliever use and using diet pills, powders, or liquids (OR = 2.2, p < .01). Results suggest significant associations between NMUPD and EWCB, with variations by sex. These findings provide directions for additional research and point to several potential identification and intervention efforts. (PsycINFO Database Record
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".