Purple Drank, Sizurp, and Lean: Hip-Hop Music and Codeine Use, A Call to Action for Public Health Educators
Bibliographic record
Abstract
The opioid epidemic continues to create various public health challenges in the United States. Non-medical use of opioids is increasing at alarming rates and has been glamorized through popular media including television, movies, and music. One particular area of concern is the promotion in hip-hop music of the use of codeine mixed with promethazine, also known as “lean.” In recent years, this drug combination has proven to be lethal with many hip-hop artists dying from overdoses involving lean while others have suffered from adverse health consequences such as seizures. Because the hip-hop music audience is primarily comprised of youth who often represent vulnerable and social disadvantaged populations, it is imperative to develop interventions that counteract the negative influence of such songs. The purpose of this study is to review the lyrics of popular hip-hop songs that mention lean and determine common themes within these songs that can be used to guide future interventions. To identify these themes, the lyrics of 40 hip-hop songs were evaluated by four independent coders. 8 themes emerged and the frequency in which these themes appeared in the song lyrics was calculated. These themes are the use of lean with another drug (37.5%), the general mention of lean without a connection to a behavior, activity, emotion, or another substance (27.5%), the use of lean during sexual activity (15%), the use of lean with soda (12.5%), the use of lean to help with sleep (5%), the use of lean as an alternative to alcohol (5%), the use of lean while driving (5%), and the use of lean for mental distress (5%). These results demonstrate that there are various aspects of lean use that require further investigation. Furthermore, these results serve as a call to action for public health practitioners to create culturally tailored interventions to address this issue.
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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".