A Systematic Review of Elderly Suicide Prevention Programs
Bibliographic record
Abstract
BACKGROUND: Suicide rates are highest among the elderly, yet research on suicide prevention in old age remains a much-neglected area. AIMS: We carried out a systematic review to examine the results of interventions aimed at suicidal elderly persons and to identify successful strategies and areas needing further exploration. METHODS: Searches through various electronic databases yielded 19 studies with an empirical evaluation of a suicide prevention or intervention program designed especially for adults aged 60 years and older. RESULTS: Most studies were centered on the reduction of risk factors (depression screening and treatment, and decreasing isolation), but when gender was considered, programs were mostly efficient for women. The empirical evaluations of programs attending to the needs of high-risk older adults seemed positive; most studies showed a reduction in the level of suicidal ideation of patients or in the suicide rate of the participating communities. However, not all studies used measures of suicidality to evaluate the outcome of the intervention, and rarely did they aim at improving protective factors. CONCLUSIONS: Innovative strategies should improve resilience and positive aging, engage family and community gatekeepers, use telecommunications to reach vulnerable older adult, and evaluate the effects of means restriction and physicians education on elderly suicide.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".