Depression and the relationship between sleep disturbances, nightmares, and suicidal ideation in treatment-seeking Canadian Armed Forces members and veterans
Bibliographic record
Abstract
BACKGROUND: Research on the relationship between insomnia and nightmares, and suicidal ideation (SI) has produced variable findings, especially with regard to military samples. This study investigates whether depression mediated the relationship between: 1) sleep disturbances and SI, and 2) trauma-related nightmares and SI, in a sample of treatment-seeking Canadian Armed Forces (CAF) personnel and veterans (N = 663). METHOD: Regression analyses were used to investigate associations between sleep disturbances or trauma-related nightmares and SI while controlling for depressive symptom severity, posttraumatic stress disorder (PTSD) symptom severity, anxiety symptom severity, and alcohol use severity. Bootstrapped resampling analyses were used to investigate the mediating effect of depression. RESULTS: Approximately two-thirds of the sample (68%; N = 400) endorsed sleep disturbances and 88% (N = 516) reported experiencing trauma-related nightmares. Although sleep disturbances and trauma-related nightmares were both significantly associated with SI on their own, these relationships were no longer significant when other psychiatric conditions were included in the models. Instead, depressive symptom severity emerged as the only variable significantly associated with SI in both equations. Bootstrap resampling analyses confirmed a significant mediating role of depression for sleep disturbances. CONCLUSIONS: The findings suggest that sleep disturbances and trauma-related nightmares are associated with SI as a function of depressive symptoms in treatment-seeking CAF personnel and veterans. Treating depression in patients who present with sleep difficulties may subsequently help mitigate suicide risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".