What Are We Aiming For? Comparing Suicide by Firearm in Toronto With the Five Largest Metropolitan Areas in the United States
Bibliographic record
Abstract
Abstract. Background: US suicide rates correlate with firearm availability. Little is known about variability in rates across countries. Aims: To observe the relationship between firearm/overall suicide rates in Toronto, Canada, and the five most populous US metropolitan areas. Method: Centers for Disease Control suicide rates by age and sex for New York, Los Angeles, Chicago, Dallas-Fort Worth, and Houston metropolitan areas were compared with equivalent data for Toronto (1999–2015). Results: Suicide rates by firearm, per 100,000 population, ranged from 0.45 in Toronto to 6.03 in Houston while rates by other methods ranged from 4.34 in Dallas-Fort Worth to 7.11 in Toronto. Overall rates of suicide ranged from 6.14 in New York to 10.45 in Houston. The two cities with the highest firearm suicide rates, Dallas-Fort Worth and Houston, also had much higher overall rates. Firearm suicides were most common in men over the age of 65 in all cities. Limitations: This study could not account for cultural differences between cities/countries. Conclusion: Much higher overall rates of suicide observed for Dallas-Fort Worth and Houston appear to be associated with high rates of suicide by firearm. Advocacy for means safety should target cities with high rates of firearm suicide and, in particular, elderly men.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".