An Account From a Sexual Assault Nurse Examiner on Caring for a Transgender Survivor: A Case Report
Bibliographic record
Abstract
ABSTRACT: Transgender individuals represent a gender minority population that has been underserved within the healthcare system and underrepresented in population health and sexuality research, specifically as it pertains to sexual assault. This case report aims to explore how sexual assault nurse examiners (SANEs) approach the care of transgender people who have survived sexual assault. Key components and findings related to the SANE's encounter will be examined including an evaluation of the biases and assumptions held by the SANE and other healthcare providers. Concepts such as cisnormativity, heteronormativity, and intersectionality will be examined in terms of how these can shape the experience of the survivor, influence the care provided by SANEs, and interact with gender stereotypes and nonaffirming practices faced by transgender people. This case report highlights the importance of acknowledging and undermining nursing approaches that can (re)traumatize sexual assault survivors and explores ways in which SANEs can help to shift views of gender and bodies with the goal of providing better care for gender minority populations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".