Backing the Blue: Trauma in Law Enforcement Spouses and Couples
Bibliographic record
Abstract
Objective To explore the lived experiences of secondary trauma among partners of law enforcement professionals (LEPs). Background Stress is a common occurrence for LEPs. Although research suggests that LEPs are directly affected by trauma exposure, few studies focus on the secondary trauma of partners or spouses of LEPs. Method Utilizing transcendental phenomenological inquiry, in‐depth qualitative interviews were conducted with a purposeful sample of eight spouses of law enforcement recruited from community groups and police departments. Results The results revealed three overarching themes of how participants experienced being partnered with an LEP: (a) types of trauma exposure, (b) the ripple impact of trauma, and (c) strength of couples and how they cope with trauma. Conclusion Findings suggest that spouses are both affected by trauma and serve a supportive role to LEPs following trauma exposure. Because secondary trauma can exacerbate existing difficulties in communication and emotional intimacy within couples' relationships, a greater understanding of the impact of trauma on law enforcement couples may lead to greater resources to help support couples wherein one individual is directly exposed to work‐related trauma. Implications Family professionals should promote healthy responses and coping among law enforcement couples following exposure to traumatic events.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".