Comparison of Fatal Recreational Drug Overdoses between Celebrities and Non-Celebrities
Bibliographic record
Abstract
Previous studies have examined drug overdoses among celebrities, but not in comparison to the general population. This study’s goal was to analyze whether celebrities have higher fatal overdose rates from recreational drug use than the non-celebrity population. It is often presumed that celebrities engage in more drug use to cope with their stressful and taxing lifestyles. To test this claim, we gathered a list of American celebrities that fatally overdosed on drugs from 1999 to 2017 (inclusive), as well as the number of overdoses in the general American population during this time frame. Certain drugs of interest were kept and less commonly occurring drugs that resulted in overdose were excluded, leaving us with opioids, heroin, cocaine, benzodiazepines, psychostimulants, and antidepressants. Descriptive statistics of both populations including gender and specific professions of celebrities were collected. Then, an independent samples t-test was used to discover if there was a significant difference between fatal overdoses for the celebrity versus non-celebrity population in general and for each drug listed previously from the years 1999 to 2017. Pearson’s correlation analysis was used to find if there was a difference in the yearly trend of overdoses for celebrities versus non-celebrities during the same time range. Descriptive statistics demonstrated that males comprised 62.9% of fatal overdoses for non-celebrities and 73.5% for celebrities, and musicians (24.3%), athletes (23.6%), and actors (17.6%) tend to overdose the most in terms of celebrity professions. In addition, the results from the t-test showed that non-celebrities had not fatally overdosed at significantly different rates than celebrities from 1999 to 2017. as well as overdosed at no significantly different rate for each individual drug than celebrities during this time frame. However, the exceptions were any opioids and benzodiazepines, for which the former group overdosed at a significantly higher rate. Pearson’s correlation analysis yielded an insignificant negative correlation between fatal overdoses and years passed between 1999 to 2017 for celebrities, and a significant positive correlation between fatal overdoses and years passed for non-celebrities. The judgmental heuristics may make us believe that more celebrities fatally overdose than non-celebrities, and that this presumption could potentially be problematic because celebrities have a massive influence on society, which could lead the general population to engage in these self-destructive behaviours themselves.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".