295 – Gender Differences in Determinants of Suicidal Ideation in French Speaking Community Living Older Adults in Canada
Bibliographic record
Abstract
Background: Determinants of suicidal ideation in females and males may differ in older adults. Objectives: To ascertain gender specific determinants of suicidal ideation. Methods: Data used in this study was from the ESA survey on a large representative sample of community dwelling older adults (n=2494). Multivariate logistic regression analysis was used to study the association between suicidal ideation, mental health service and antidepressant use and a number of clinical and socio-demographic factors. Results: The prevalence of suicidal ideation reached 6.3%. The findings of this study show that the presence of suicidal ideation in females is associated with younger age, single or widowed status, the reporting of daily life stressors and chronic conditions as well as the presence of depression. In males, suicidal ideation is associated with older age, single or widowed status and depression. Furthermore, suicidal ideation is significantly associated with antidepressant use in females but not males and this after controlling for a number of clinical factors. Conclusion: Although no gender differences are observed between suicidal ideation and mental health service use, females with suicidal ideation are more likely to be dispensed antidepressants than males with suicidal ideation. The more prevalent use of antidepressants in females with suicidal ideation may lead to better management of symptoms related to depression and their consequences at an earlier stage of the disorder.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".