Daily hassles, physical illness, and sleep problems in older adults with wishes to die
Bibliographic record
Abstract
BACKGROUND: Factors associated with the wish to die should be investigated in order to gain more opportunities for preventive interventions targeting older adults at risk for suicide. The goal of the research was to study the prevalence and associated factors of wishes to die in older adults living in the community using the data from a survey on the prevalence of mental disorders in this population. METHODS: With a representative sample of community living older adults aged 65 years and over (N = 2777), we compared individuals with the wish to die (n = 163) to those without the wish to die on the basis of the presence and severity of daily hassles, physical illness, and sleep quality. RESULTS: Logistic regression revealed that when depression and sociodemographic variables were held constant, self-rated physical health, number of chronic illnesses, number and intensity of daily hassles, as well as sleep problems were significantly associated with the wish to die in older adults. Painful illnesses and daytime dysfunction due to sleep problems were also associated factors with the wish to die. CONCLUSION: Since desire for death is the first step into the suicidal process, health professionals should seriously consider the important and unique contribution of these variables in order to have more opportunities for detection and intervention.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".