Exploring How Sexual Assault Nurse Examiners Practise Trauma-Informed Care
Bibliographic record
Abstract
BACKGROUND: Sexual violence is a term describing sexual acts where consent is not freely given. Registered nurses employed as sexual assault nurse examiners (SANEs) provide care to address the medical and legal needs of victims/survivors of sexual violence. Trauma-informed care (TIC) is an approach recommended when caring for individuals who have experienced trauma. PURPOSE: The study purpose was to understand how SANEs incorporate trauma-informed approaches in the care of adult and postpubescent adolescent victims/survivors of sexual violence. METHODS: Eight SANEs were purposively recruited to participate in online semistructured interviews. Interview data were analyzed using qualitative interpretive description. RESULTS: Six themes emerged from the analysis: (a) the importance of understanding the patient's experience; (b) personalized connection: developing a safe nurse-patient relationship; (c) choice: the framework of how we do things; (d) rebuilding strengths and skills to support healing and posttraumatic growth; (e) a wonderful way to practise: facilitators and benefits of trauma-informed practice; and (f) challenges to trauma-informed practice. CONCLUSIONS: These findings indicate the perceived value of TIC and the need for enhanced support of providers who deliver TIC. More research is warranted to strengthen the evidence about trauma-informed practice in SANE programs and across healthcare settings.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".