Missing and murdered Indigenous women and girls: A case for abuse screening in at‐risk paediatric populations
Notice bibliographique
Résumé
American Indian/Alaska Native (AI/AN) people experience the highest rates of homicide and violence in the USA and Canada.1 This propensity for violence and sexual crimes has manifested as the crisis referred to as Missing and Murdered Indigenous Women and Girls, with more than 5000 young women having suffered homicide, kidnapping or trafficking in the USA.2 AI children are not exempt from these statistics. When compared to non-AI populations there is a statistically significantly higher proportion of AI/AN reporting of emotional, physical and sexual abuse.3 This same disparity is also true regarding emotional and physical neglect.3 Abuse and violence affect all communities, but as paediatric providers, many of the patients we treat lack the vocabulary, maturity or both to express their needs, rendering them particularly vulnerable. These conversations can be difficult, especially when there is fear that the questions asked might alienate or offend the families we are trying to serve. Medical trainees are often uncomfortable in screening for child abuse, particularly, during encounters where the signs are subtle or unrelated to the chief complaint. Medical schools and residency programmes often lack the curriculum needed to educate trainees on how to approach suspected abuse cases or the resources available to families.4 Additionally, the lack of a standardised approach to survey for abuse further complicates effective recognition and management. However, evidence suggests that residents who learn to address family psychosocial issues, including violence and abuse, are more likely to screen for these issues in practice and ultimately connect patients with needed resources.4 Several validated screening tools, including the International Society for Prevention of Child Abuse and Neglect (IPSCAN) Child Abuse Screening Tool for Children (ICAST-C) and Escape Form have demonstrated effectiveness at screening children for abuse.5, 6 The Escape Form evaluates risk based on the patient's history, the development of the child, the appropriateness of interaction between the child and caretakers, the consistency of the physical examination compared with the history, if there was a delay in seeking care and other safety concerns.6 Results from this form provide an ‘at-risk score’ that creates an objective manner of evaluation that can allow for consistency in evaluation. Review of this model has demonstrated an increase in abuse screening, trainee comfort with screening and most importantly, an increase in detection of cases that is five times higher when compared to cases of children that were not screened.6 The American Academy of Pediatrics offers several resources through ‘The Resilience Project’ aimed at addressing violence and toxic stress at both the medical practice and policy levels.7 This online resource provides tools for families, care givers and providers who treat children and youth exposed to violence.7 The implementation of the ‘Training Toolkit’ may prove valuable to trainees and early career physicians, helping them to learn the different types of violence that our paediatric patients may experience. The expanding education on patient and family-centred communication throughout clinical training may better equip future paediatricians to identify abuse and develop the confidence to start the appropriate dialogue to curb its effects. This national crisis of violence against AI/AN women and girls challenges paediatricians to find ways to address the gap in protecting girls from becoming the missing and the murdered. Screening both for abuse and family wellness is a critical step for paediatricians in addressing this crisis. Gaining the confidence to implement these skills can be undoubtedly challenging. Structured teaching and guidance can empower residents to identify abuse and will be a life-changing event for trainees and the families we treat.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».