Prescription Tranquilizer/Sedative Misuse Motives Across the US Population
Bibliographic record
Abstract
OBJECTIVES: Roughly 6.5 million US residents engaged in prescription tranquilizer/sedative (eg, benzodiazepines, Z-drugs) misuse in 2018, but tranquilizer/sedative misuse motives are understudied, with a need for nationally representative data and examinations of motives by age group. Our aims were to establish tranquilizer/sedative misuse motives and correlates of motives by age cohort, and whether motive-age cohort interactions existed by correlate. METHODS: Data were from the 2015 to 2018 US National Survey on Drug Use and Health, with 223,520 total respondents (51.5% female); 6580 noted past-year prescription tranquilizer/sedative misuse motives (2.4% overall, 50.3% female). Correlates included substance use (eg, opioid misuse), mental (eg, suicidal ideation) and physical health variables (e.g., inpatient hospitalization). Design-based, weighted cross-tabulations and logistic regression analyses were used, including analyses of age cohort-motive interactions for each correlate. RESULTS: Prescription tranquilizer/sedative misuse motives varied by age group, with the highest rates of self-treatment only motives (ie, sleep and/or relax) in those 65 and older (82.7%), and the highest rates of any recreational motives in adolescents (12-17 years; 67.5%). Any tranquilizer/sedative misuse was associated with elevated odds of substance use, mental health, and physical health correlates, but recreational misuse was associated with the highest odds. Age-based interactions suggested stronger relationships between tranquilizer/sedative misuse and mental health in adults 50 and older. CONCLUSIONS: Any tranquilizer/sedative misuse signals a need for substance use and mental health screening, with intervention needs most acute in those with any recreational motives. Older adult tranquilizer/sedative misuse may be more driven by undertreated insomnia and anxiety/psychopathology than in younger groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".