Unintentional injury deaths among youth in Ontario, Canada from 2000 to 2015: Rates are falling but there are caveats
Bibliographic record
Abstract
Youth may be susceptible to certain types of unintentional injuries that place them at risk of death. The objective of this research was to study trends in these deaths among youth (age 15 to 24) in the Canadian province of Ontario between 2000 and 2015. It is the first study to directly assess intra‐provincial trends. Our analysis of Statistics Canada's Vital Statistics – Death Database from 2000 to 2015 finds unintentional injury death rates among youth fell during this period and particularly in Ontario, where rates were significantly lower than other regions. The motor vehicle traffic death rate fell further in that province than it did nationally. The largest urban areas in the province had the lowest death rates, while several Census Divisions in the “near” north (including parts of “cottage country”) and “far” north had the highest. The unintentional injury death rate as a result of drugs and alcohol (particularly related to opioids) increased during this period, particularly in “North/Remote” areas of Ontario. To deal with the rise in drug‐ and alcohol‐related death rates, further consideration can be given to the unmeasured factors that may contribute to the use of dangerous drugs among youth.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".