Prevalence of comorbid chronic pain and mental health conditions in Canadian Armed Forces active personnel: analysis of a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Chronic pain conditions and mental disorders have high prevalence rates in military populations. However, few investigations have examined the comorbidity between chronic pain conditions and specific mental disorders among Canadian active military personnel. METHODS: We conducted a secondary analysis of data from the 2013 Canadian Forces Mental Health Survey (CFMHS) concerning the population of regular members. Diagnostic interviews assessed the presence of mental disorders, and participants self-reported chronic pain conditions (i.e., arthritis, back problems, musculoskeletal conditions, migraines) and indicators of pain severity. We used multiple logistic regressions to assess associations between chronic pain conditions and mental disorders. We used cross-tabulations to assess the prevalence of pain severity indicators in comorbid relationships compared with the chronic pain condition alone. We used moderation analyses to examine the interactions between pain condition by pain severity, and pain condition by activity limitation, respectively, on mental disorders. RESULTS: = 1761) of military personnel reported having chronic pain. In the fully adjusted model, all assessed pain conditions were significantly associated with posttraumatic stress disorder (PTSD) (odds ratio [OR] range 1.86-2.55), and several pain conditions were associated with major depressive episode, generalized anxiety disorder and panic disorder. Back problems were significantly associated with all mental disorders apart from alcohol use disorders (OR range 1.40-2.17). Cross-tabulations showed higher prevalence estimates of endorsement for pain severity indicators among pain conditions and comorbid mental disorders, compared with pain conditions alone. Formal moderation analyses showed a significant relationship between migraine and activity limitation on PTSD. INTERPRETATION: Chronic pain conditions are prevalent and co-occur with mental disorders among Canadian regular force members. Greater understanding of these chronic pain conditions and mental disorders and their impact on people's abilities to adapt to both military and civilian life is needed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".