Tracing How the Emergence of Chronic Pain Affects Military Identity: A Narrative Inquiry of Pain Trajectories Among Canadian Veterans
Bibliographic record
Abstract
Background/Objectives: Military identity serves as a foundational lens through which service members navigate the events of everyday military and civilian life. However, the very process that cultivates a sense of unity and purpose can be a double-edged sword in civilian life. Although the prevalence and transition needs are known, few studies have explored how chronic pain specifically disrupts military identity in depth. This qualitative study explores three distinct trajectories through which Veterans with chronic pain experience identity change. Methods: This study used narrative inquiry involving two sets of in-depth interviews with 20 Veterans. Reflexive thematic analysis was employed to describe and differentiate three distinct trajectories of chronic pain. Results: Veterans with chronic pain experience identity change through three overlapping pain trajectories: (1) traumatic injury -> immediate discharge; (2) misdiagnosed/non-traumatic injury -> delayed discharge; and (3) cumulative wear and tear -> gradual discharge. Regardless of trajectory, chronic pain consistently disrupted military identity and forced Veterans to confront tensions between institutional expectations of stoicism and combat readiness and the physical realities of chronic pain during military service. Those interviewed described experiencing fragmented institutional support, uneven access to care, and the systemic invalidation of pain that did not conform to military ideals. Conclusions: These findings underscore the need for Veteran-centred approaches, including responsive services, comprehensive pain science education throughout military careers, early detection of conditions that can lead to chronic pain, and flexible care pathways tailored to the nuances of each pain trajectory and grounded in military culture and lifestyle.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".