No Sky Too High, No Sea Too Rough: Qualitative Investigation of Resilience and Suicide in Special Operations Forces Service Members
Bibliographic record
Abstract
Purpose To investigate resilience in American Special Operations Forces (SOF) personnel. Method A qualitative descriptive exploratory design was used to interview Special Forces and Navy SEAL participants about their perspectives on and experiences of resilience. Assumptions that high resilience inversely correlates with suicide risk in SOF drove our primary research questions and study focus. Questions were based on Holling's theory of ecological resilience. Results Participants provided insightful and detailed data of their resilience and were often self-effacing or self-critical. Responses indicated that although quite resilient, SOF personnel express their resilience in ways known to become pathological and precipitate suicidality if left undetected. Extracted subthemes indicated commitment to others over self and a nexus of trait variables linked to suicidality. Estimated neurotrauma from repetitive blast exposures should be incorporated in future models. Conclusion Findings challenge prevailing beliefs that dysfunctional behaviors and suboptimal resilience drive SOF suicide. Results herein justify future research and changes to command postures and U.S. Department of Defense initiatives regarding relationships between and among variables of resilience, neurotrauma, and suicide in SOF. [ Journal of Psychosocial Nursing and Mental Health Services, 63 (5), 26–38.]
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".