Identifying and responding to child maltreatment when delivering family‐based treatment—A qualitative study
Bibliographic record
Abstract
INTRODUCTION: This study describes practitioner strategies, perceptions, experiences with identifying and responding to child emotional abuse (CEA) and child exposure to intimate partner violence (CEIPV) when providing Family-Based Treatment (FBT) to children and adolescents with eating disorders. METHOD: Using qualitative interpretive description, this study recruited a purposeful sample of practitioners (N = 30, 90% female) implementing FBT for adolescent eating disorders. Semi-structured interviews focused on eliciting their perspectives regarding identifying and responding to CEA and CEIPV in practice. Interviews were conducted over the phone, were audio recorded, transcribed verbatim, and coded using conventional content analysis. Interim member checking, the thoughtful clinician test, and coding memos were used to ensure the integrity of the analysis. RESULTS: Participants were 31-57 years old and practicing FBT in five countries. Three data patterns emerged: (a) perceptions of child maltreatment prevalence and identification; (b) complicating factors; and finally (c) strategies to support family-based work. Practitioners described important considerations for CEA and CEIPV identification, as well as possible FBT adaptations that can support the safety of children and adolescents while simultaneously ensuring the treatment of the eating disorder. CONCLUSIONS: Practitioners describe a need for additional training to identify and respond to CEA and CEIPV within FBT and within practice more broadly. There is a need for trials that detail the appropriateness and efficacy of FBT for patients experiencing CEA and/or CEIPV.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".