Canada-Wide Effect of Regulatory Warnings on Antidepressant Prescribing and Suicide Rates in Boys and Girls
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of the Health Canada regulatory warnings regarding antidepressant (AD) prescribing on suicide rates in boys and girls under the age of 18 and aged 18 to 19 years in Canada between 2004 and 2009. We hypothesized that an increase in suicide rates would be specific to girls, reflecting higher AD prescribing rates in girls than boys. METHOD: We graphed and tested the difference between Canada-wide suicide rates before and after the regulatory warning periods (either from 1995 to 2006 or from 1995 to 2009) in boys and girls under the age of 18 or aged 18 to 19 years. For comparison with prior studies, we estimated rate ratios and 95% confidence intervals using either Poisson regression or negative binomial regression. RESULTS: There was no statistically significant increase in suicide rates in girls under the age of 18, or aged 18 to 19 years in response to the AD regulatory warnings. In boys under the age of 18 or aged 18 to 19 years, suicide rates declined after 2003. CONCLUSIONS: We did not find increased rates of suicide after the AD regulatory warnings in boys or girls under the age of 18 or aged 18 to 19 years in Canada-wide rates. However, this does not rule out the possibility that such an effect occurred in some jurisdictions in girls and (or) the regulatory warnings prevented the trend toward declining suicide rates. Factors influencing the downward trend in boys merit further attention.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".