Patient and Healthcare Provider Satisfaction with Sexual Assault Nurse Examiners (SANEs): A Systematic Review
Bibliographic record
Abstract
Background/Objectives: The World Health Organization (WHO) estimates that one in three women worldwide has experienced physical or sexual violence. In countries like the US, UK, and Canada, victims are often cared for by sexual assault nurse examiners (SANEs), who are trained to conduct forensic exams and offer emotional support, reducing the risk of retraumatisation. Thus, the aim of this study was to describe the satisfaction of patients and healthcare professionals with SANEs’ services. Methods: A systematic review was conducted by searching the PubMed, Web of Science, and Scopus databases, selecting studies that focused on patient and healthcare provider satisfaction with SANEs’ services. Results: In total, nine studies meeting the inclusion and exclusion criteria were analysed. Of these, 55% focused on healthcare provider satisfaction, while 44% examined the experiences of sexual assault survivors. All studies examining patient satisfaction with the care provided by SANE professionals (n = 3) reported satisfaction levels exceeding 90%, with many users highly recommending their services. Conclusions: The role of sexual assault nurse examiners is crucial in providing victims with a safe environment and quality of care, and in reducing the risk of retraumatisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".