Emergency department visits for self-harm in adolescents after release of the Netflix series ‘13 Reasons Why’
Bibliographic record
Abstract
Objective: To determine whether the release of the first season of the Netflix series ‘13 Reasons Why’ was associated with changes in emergency department presentations for self-harm. Methods: Healthcare utilization databases were used to identify emergency department and outpatient presentations according to age and sex for residents of Ontario, Canada. Data from 2007 to 2018 were used in autoregressive integrated moving average models for time series forecasting with a pre-specified hypothesis that rates of emergency department presentations for self-harm would increase in the 3-month period following the release of 13 Reasons Why (1 April 2017 to 30 June 2017). Chi-square and t tests were used to identify demographic and health service use differences between those presenting to emergency department with self-harm during this epoch compared to a control period (1 April 2016 to 30 June 2016). Results: There was a significant estimated excess of 75 self-harm-related emergency department visits (+6.4%) in the 3 months after 13 Reasons Why above what was predicted by the autoregressive integrated moving average model (standard error = 32.4; p = 0.02); adolescents aged 10–19 years had 60 excess visits (standard error = 30.7; p = 0.048), whereas adults demonstrated no significant change. Sex-stratified analyses demonstrated that these findings were largely driven by significant increases in females. There were no differences in demographic or health service use characteristics between those who presented to emergency department with self-harm in April to June 2017 vs April to June 2016. Conclusions: This study demonstrated a significant increase in self-harm emergency department visits associated with the release of 13 Reasons Why. It adds to previously published mortality, survey and helpline data collectively demonstrating negative mental health outcomes associated with 13 Reasons Why.
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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 | 0.000 |
| 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) | 0.001 | 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".