ArticleAddiction research & theory2016
Nonmedical use of prescription drugs in emerging adulthood: differentiating sex from gender.
Article in Addiction research & theory, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Systematic review of the use of prescription and non-prescription psychotropic drugs and their relation with mental health in university population.Frontiers in psychology · 2025Pooled it
- Pooled it
- Prescription opioid misuse among adolescents and emerging adults in the United States: A scoping review.Preventive medicine · 2020Article
- Young Adult Women and Alcohol-Related Problems: The Key Role of Multidimensional Feminine Norms.Substance abuse : research and treatment · 2020Review
- Non-medical use of prescription drugs by young men: impact of potentially traumatic events and of social-environmental stressors.European journal of psychotraumatology · 2018Article
- Gender Policing During Childhood and the Psychological Well-Being of Young Adult Sexual Minority Men in the United States.American journal of men's health · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Male-female variations in health-behavior continue to be of national and international significance with men generally being more likely to be engaged in behaviors that enhance risk across an array of preventable diseases and injuries as well as premature deaths. The literature has identified non-medical use of prescription drugs (NMUPD) as a developing and particularly dangerous substance use behavior among college students. The literature has reported sex differences (male; female) in NMUPD but has yet to explain how gender-orientation (e.g., masculine, feminine) might impact NMUPD. The purpose of this study is to address this gap by examining the influence of gender-orientation on NMUPD. Using survey data collected during the 2013-2014 academic year from a convenience sample of college students at a mid-sized Midwestern university, we examine the association of gender-orientation with NMUPD (N=796). To do this, we separate masculine and feminine scales from the BEM Sex Role Inventory and use logistic regression to test whether masculine or feminine gender characteristics influence the likelihood of NMUPD (lifetime measure of any use and by category). This analysis shows that self-identified characteristics associated with masculinity increase the odds of NMUPD while femininity is associated with lower odds of NMUPD. Findings from this study increase our knowledge of gender-orientation and sex interactions as factors that might influence NMUPD thus demonstrating the importance of differentiating sex from gender-orientation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.