The Experiences of Women Canadian Armed Forces Service Members and Veterans with Comorbid Chronic Pain and PTSD: A Qualitative Inquiry
Bibliographic record
Abstract
Chronic pain and posttraumatic stress disorder (PTSD) frequently co-occur among service members and veterans. Evidence suggests that women service members and veterans are more likely to experience comorbid chronic pain and PTSD than their men counterparts. Theoretical models of comorbid chronic pain and PTSD suggest the conditions may maintain and exacerbate each other, creating a unique clinical interaction that worsens outcomes and complicates treatment. Given women military personnel experience these comorbid conditions at elevated rates, there is a particular need to illuminate their narratives. The current study aimed to qualitatively explore the experiences of women service members and veterans with comorbid chronic pain and PTSD to identify potential connections between chronic pain and PTSD. It also sought to examine issues that affect military women more acutely (e.g. military sexual trauma) that may be uniquely impacting their experience of the conditions. Nineteen Canadian Armed Forces (CAF) service members and veterans participated in one to one and a half hour semi-structured interviews about their experiences of comorbid PTSD and chronic pain. This study selected the transcripts of women service members and veterans (n = 6) and applied interpretive description, a qualitative methodology that aims to produce interpretive insights that enhance clinical understanding. Thematic analysis was used to extract exemplars to identify themes within the data. The analysis identified three main themes. The first theme described ways in which the conditions were related, such as one condition triggering the other. The second theme illustrated participants’ feelings of betrayal by the military, which complicated the way they experienced their pain and trauma. The third theme described participants’ struggles with accepting their pain and trauma, which was layered by feelings of betrayal, military culture, and self-identity. By capturing lived experiences, this study provided crucial insight into women CAF service members’ and veterans’ unique challenges in living with chronic pain and PTSD. Findings reflected portions of theoretical models of comorbid chronic pain and PTSD and identified novel ways in which the conditions were linked. This study offers clinical perspectives to inform the care of Canadian women service members and veterans living with these conditions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".