Association of Logic’s hip hop song “1-800-273-8255” with Lifeline calls and suicides in the United States: interrupted time series analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To assess changes in daily call volumes to the US National Suicide Prevention Lifeline and in suicides during periods of wide scale public attention to the song "1-800-273-8255" by American hip hop artist Logic. DESIGN: Time series analysis. SETTING: United States, 1 January 2010 to 31 December 2018. PARTICIPANTS: Total US population. Lifeline calls and suicide data were obtained from Lifeline and the Centers for Disease Control and Prevention. MAIN OUTCOME MEASURES: Daily Lifeline calls and suicide data before and after the release of the song. Twitter posts were used to estimate the amount and duration of attention the song received. Seasonal autoregressive integrated moving average time series models were fitted to the pre-release period to estimate Lifeline calls and suicides. Models were fitted to the full time series with dummy variables for periods of strong attention to the song. RESULTS: In the 34 day period after the three events with the strongest public attention (the song's release, the MTV Video Music Awards 2017, and Grammy Awards 2018), Lifeline received an excess of 9915 calls (95% confidence interval 6594 to 13 236), an increase of 6.9% (95% confidence interval 4.6% to 9.2%, P<0.001) over the expected number. A corresponding model for suicides indicated a reduction over the same period of 245 suicides (95% confidence interval 36 to 453) or 5.5% (95% confidence interval 0.8% to 10.1%, P=0.02) below the expected number of suicides. CONCLUSIONS: Logic's song "1-800-273-8255" was associated with a large increase in calls to Lifeline. A reduction in suicides was observed in the periods with the most social media discourse about the song.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it