What Are We Aiming For? Comparing Suicide by Firearm in Toronto With the Five Largest Metropolitan Areas in the United States
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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