A Canadian Sexual Assault Nurse Examiner’s Personal Reflection and Ongoing Questioning of Vicarious Trauma
Bibliographic record
Abstract
ABSTRACT Vicarious trauma (VT) is a concept that has been recognized in nursing. Although research has extensively explored signs, symptoms, and risks associated with VT, there is a notable gap in the literature concerning the personal lived experiences of sexual assault nurse examiners (SANEs). Objective The objective of this article was to provide a first-person account of the vulnerable aspects of my life where I questioned if VT had influenced my thought processes and to uncover the potential health risks associated with exposure to patients' repeated stories of trauma. I questioned whether I was experiencing VT or other disorders such as burnout, posttraumatic stress disorder, or compassion fatigue. The scholarly literature was reviewed after my personal reflection to analyze my personal experiences and to gain clarity on how VT and/or other related concepts may impact the professional and personal lives of SANEs. Methods Methods used to produce my personal stories have been done via self-reflection and journaling. Three stories that I believe may resemble VT are shared and analyzed vis-à-vis the literature. My personal vignettes are compared with signs and symptoms of VT and illustrate how they may manifest in the daily lives of SANEs. Implications To date, researchers have neglected to explore concrete examples of the personal depth VT may exhibit in an individual's life. By disclosing and synthesizing my personal stories, I hope to encourage SANEs to be open about their experiences, spread awareness and prevention strategies regarding VT, and, ultimately, further enhance well-being and promote increased longevity in their careers.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".