Providing trans-affirming care for sexual assault survivors: An evaluation of a novel curriculum for forensic nurses
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».