From not knowing, to knowing more needs to be done: health care providers describe the education they need to care for sex trafficked patients
Notice bibliographique
Résumé
BACKGROUND: Sex trafficking is highly prevalent, pernicious, and under-recognized. When an individual is trafficked for the purpose of sexual exploitation within the borders of a single country, it is termed domestic sex trafficking. Sex trafficked persons can experience severe physical and mental health outcomes requiring medical attention and treatment. However, health care providers often fail to identify sex trafficked patients, missing opportunities to provide needed care and support. METHODS: In this qualitative study, we interviewed 31 health care providers (physicians, nurses, and social workers) working in Ontario, Canada to learn what they identified as their specific education and training needs to recognize and care for sex trafficked persons. Interviews were conducted over Zoom, recorded, and transcribed. Coding of the transcripts followed a standard framework for qualitative studies. Codes related to the education and training needs of providers were identified as a core issue suited to further analysis. RESULTS: Three themes related to providers' education and training needs emerged. These acknowledge basic (Foundational knowledge), as well as more specific learning needs (Navigating the encounter). The final theme, ("It just seems so much bigger than me") suggests that even with some knowledge of domestic sex trafficking, participants still experienced considerable distress and multiple challenges due to gaps in the broader system impacting the provision of appropriate care. CONCLUSIONS: Participants voiced their need for specialized sex trafficking education as well as role specific training to combat their sense of inadequacy and provide better care for their patients. Participants' education needs ranged from requiring the definition of domestic sex trafficking and the frequency of its occurrence, to the various circumstances associated with increased risk of recruitment into sex trafficking. In terms of desired training and specific skills, participants wanted to learn how to identify a person being sex trafficked, broach the subject with a patient, know what to do next including access to local resources and referrals, as well as connections to other critical services, such as legal and housing. The results can be used to inform the design and content of education and training on sex trafficking for health care providers.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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 ».