Recognizing and Responding to Child Maltreatment: Strategies to Apply When Delivering Family-Based Treatment for Eating Disorders
Bibliographic record
Abstract
Child maltreatment encompasses a constellation of adverse parental behaviors that include physical, sexual, or emotional abuse, physical or emotional neglect, as well as exposure to violence between parents. A growing body of literature indicates that exposure to child maltreatment is a significant risk factor for the development and maintenance of eating disorders (EDs) and that practitioners experience challenges related to recognizing and responding to various forms of child maltreatment in their practice. Parent-child interactions signifying possible child maltreatment can be subtle; furthermore, the emotional and behavioral symptoms associated with an ED can overlap with those linked with child maltreatment, making it difficult for practitioners to distinguish whether children's symptoms are attributable to underlying psychopathology versus exposure to child maltreatment. This challenge can be further complicated in the context of delivering family-based treatment (FBT); FBT reaffirms that there is no single cause of EDs and asserts the leadership role of parents in their child's recovery process-both of which may lead practitioners to inadvertently miss indicators of child maltreatment. In this article, we provide an overview of the evidence linking child maltreatment to EDs among children and adolescents, as well as evidence-informed strategies for practitioners to safely recognize and respond to suspected child maltreatment when delivering FBT to children and adolescents in their practice.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".