Trends and Factors Associated with Suicide Deaths in Older Adults in Ontario, Canada
Bibliographic record
Abstract
Background: Suicide in older adults is a significant overlooked problem worldwide. This is especially true in Canada where a national suicide prevention strategy has not been established. Methods: Using linked health-care administrative databases, this population-level study (2011 to 2015) described the incidence of older adult suicide (aged 65+), and identified clinical and socio-demographic factors associated with suicide deaths. Results: The findings suggest that suicide remains a persistent cause of death in older adults, with an average annual suicide rate of about 100 per million people over the five-year study period. Factors positively associated with suicide vs. non-suicide death included being male, living in rural areas, having a mental illness, having a new dementia diagnosis, and having increased emergency department visits in the year prior to death; whereas, increased age, living in long-term care, having one or more chronic health condition, and increased interactions with primary health care were negatively associated with a suicide death. Conclusion: Factors associated with suicide death among older adults highlighted in this study may provide better insights for the development and/or improvement of suicide prevention programs and policies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".