Facing Adversity and Factors Affecting Resilience: A Qualitative Analysis of the Lived Experiences of Canadian Special Operations Forces
Bibliographic record
Abstract
Special Operations Forces (SOF) personnel are required to withstand considerable physical and psychological hardship. Research examining resilience and mental health among SOF personnel is limited and has provided mixed results; in addition, minimal research has been undertaken on the subjective experiences of adversity and the process of resilience among SOF personnel. This unique qualitative study describes the lived experience of Canadian SOF personnel, the challenges they face, and the factors they believe impact their resilience. Seventy Canadian SOF personnel participated in in-depth, semistructured interviews. A thematic analysis of the interviews revealed that operational demands, paired with an organizational culture of performance, were important stressors for most participants, negatively affecting both themselves and their families. SOF organizations select members with resilient characteristics; however, the same characteristics that make these members resilient also lead to self-imposed pressure to perform and avoid taking time for proper recovery. Team members were reported to help such members process difficult or traumatic experiences and facilitate their seeking care. Findings provide insight into the adverse experiences that participants encountered while serving in an SOF organization and the intertwined individual, social, and organizational factors affecting their resilience. Results point to the importance of managing and mitigating the impact of high operational tempo and a culture of performance to protect the health and wellness of SOF personnel and their families.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".