Providing trans-affirming care for sexual assault survivors: An evaluation of a novel curriculum for forensic nurses
Bibliographic record
Abstract
BACKGROUND: Transgender (trans) persons experience high rates of sexual victimization, often face discrimination by healthcare providers, and may have unique and diverse needs post-victimization. However, there remains a lack of comprehensive trans-specific training among healthcare professionals, including nurses. OBJECTIVES: Our primary objective was to develop and evaluate a novel curriculum for its efficacy in improving the competence of forensic nurses in providing sensitive, informed, and appropriate healthcare services for trans survivors of sexual assault. METHODS: The curriculum was evaluated among forensic nurses working in sexual assault treatment centres across Ontario, Canada. Forty-seven nurses participated in this study, all of whom were selected by their respective programs to receive in-depth formal Sexual Assault Nurse Examiner training. Changes in participants' perceived expertise and competence in providing trans-affirming care were assessed on a 5-point Likert scale (5 being the highest level) using pre- and post-training questionnaires. Participants were asked to indicate their level of agreement with 31 competency-based statements, which were organized thematically into four domains: Initial Assessment, Medical Care, Forensic Examination, and Discharge and Referral. A clinical vignette assessed participants' demonstrated competence in providing care across four questions. RESULTS: Participants level of expertise improved significantly from pre- to post-training (Mean [M] = 1.89, Standard Deviation [SD] = 0.84 vs. M = 3.47, SD = 0.62, p< 001), as well as their competence across all content domains: initial assessment (M = 3.79, SD = 0.63 vs. M = 4.70, SD = 0.31, p < .001), medical care (M = 3.33, SD = 0.73 vs. M = 4.69, SD = 0.33, p < .001), forensic examination (M = 3.40, SD = 0.75 vs. M = 4.72, SD = 0.35, p < .001), and discharge and referral (M = 3.62, SD = 0.80 vs. M = 4.59, SD = 0.40, p < .001). There were also significant improvements in competence associated with the clinical vignette pre- to post- training (M score = 2.13, SD = 1.06 vs. M score = 3.23, SD = 0.87, p < .001). CONCLUSIONS: The success of this curriculum may have relevance to the more than 5000 members of the International Association of Forensic Nurses who practice and support forensic nursing across the globe, as well as to other healthcare professionals.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".