Missing and murdered Indigenous women and girls: A case for abuse screening in at‐risk paediatric populations
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| 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".