A narrative review: suicide and suicidal behaviour in older adults
Bibliographic record
Abstract
Globally, suicide is a public health concern that claims the lives of many each year. The complex etiology and factors contributing to the risk of suicide make it hard to predict the likelihood of death by suicide. Suicide rates have been increasing over the past 25 years in patients aged 65 years and older, and with the expected increases in the size of the older adult population and the under-detection of suicide risk, these rates may continue to increase. To mitigate and attempt to limit this expected increase, it is important to understand the risk and protective factors of suicide in older adults. This narrative review focuses on individuals above the age of 65 and encompasses relevant peer-reviewed publications from the past 25 years to cover fatal and non-fatal suicidal behaviour. It summarizes several important risk factors for suicide and suicidal behaviors while considering how risk can be detected, assessed, prevented, and mitigated. Screening methods to detect suicide and depression in older adults were examined based on their effectiveness and suitability for use in this population. Lastly, the impacts of the COVID-19 pandemic on suicide rates in older adults were described.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".