Insomnia, psychiatric disorders and suicidal ideation in a national representative sample of active Canadian Forces members
Bibliographic record
Abstract
Background Past research on the association between insomnia and suicidal ideation (SI) has produced mixed findings. The current study explored the relationship between insomnia, SI, and past-year mental health status among a large Canadian Forces (CF) sample. Method Data was obtained from the 2013 Canadian Forces Mental Health Survey (CFMHS), and included a large representative sample of Canadian Regular Forces personnel (N = 6700). A series of univariate logistic regressions were conducted to test individual associations between past-year mental health status, insomnia, and potential confounds and SI. Mental health status included three groups: 0, 1, or two or more probable diagnoses of posttraumatic stress disorder (PTSD), major depressive disorder (MDD), generalized anxiety disorder (GAD), panic disorder (PD) and alcohol abuse/dependence. Stepwise multivariate logistic regression was used to assess the relationship between insomnia and SI with mental health status as a moderator. Results 40.8% of respondents reported experiencing insomnia. Both insomnia and number of mental health conditions incrementally increased the risk of SI. However, past-year mental health status was a significant moderator of this relationship, such that for CF personnel with either no (AOR = 1.61, 1.37–1.89) or only one past-year mental health condition (AOR = 1.39, 1.12–1.73), an incremental increase in insomnia was associated with an increased likelihood of SI. However, in personnel with two or more past-year mental health disorders, insomnia was no longer significantly associated with SI (AOR = 1.04, 0.81–1.33). Conclusions Insomnia significantly increased the odds of SI, but only among individuals with no or one mental health condition. Findings highlight the importance of assessing insomnia among CF members in order to further suicide prevention efforts.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| 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".