The influence of depression-PTSD comorbidity on health-related quality of life in treatment-seeking veterans
Bibliographic record
Abstract
Objective Posttraumatic stress disorder (PTSD) and depression substantially impair health-related quality of life (HRQOL) for many Canadian Armed Forces (CAF) veterans. Although PTSD and depression are highly comorbid, little is known about whether the disorders may interact in their association with HRQOL. We sought to investigate whether depressive symptoms modify the relation between PTSD and HRQOL in treatment-seeking veterans.Method We accessed the clinical data of 545 CAF veterans aged 18 to 65 years who were seeking treatment at a specialized clinic in London, Ontario. We used hierarchical linear regression to assess the additive and multiplicative relations between depression and PTSD symptoms on HRQOL, controlling for age and alcohol/substance abuse. Simple slopes were examined to probe significant interactions.Results Probable PTSD and major depression were present in 77.4% and 85.3% of the sample, respectively, and 73.0% of the sample presented with probable PTSD-depression comorbidity. Depression symptoms significantly modified the relation between PTSD symptoms and overall mental HRQOL (β = 0.12, p <0.001, ∆R2 = 0.014), and role impairment due to emotional difficulties (β = 0.20, p <0.001, ∆R2 = 0.035). Simple slope analyses revealed the impact of PTSD was greater among those with lower depression symptoms and became weaker with greater depression symptom severity. In adjusted models, only depression was significantly associated with all mental and physical HRQOL domains; PTSD was not associated with physical HRQOL, role emotional impairment, or vitality.Conclusions For those with severe comorbid depression, PTSD symptoms were no longer associated with mental HRQOL, particularly in areas related to emotional functioning. Findings suggest the importance of targeting depression in patients presenting with PTSD-depression comorbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".