Best Practices for Interviewing Children with Intellectual Disabilities in Maltreatment Cases
Bibliographic record
Abstract
Globally, children with intellectual disabilities are at an increased risk of being victims of maltreatment compared to those without disabilities. Among the children who do disclose the abuse, limitations with communication and working memory can result in their allegation being perceived as not credible. There are several evidence-based interviewing methods available to interviewers for improving the accuracy and amount of detail in children’s testimonies, such as free-recall and cognitive load questioning. In general, these interviewing methods have been developed and tested with typically developing populations, and do not take into consideration the needs of children with intellectual disabilities. Further, there is very little empirical work to guide forensic interviews with intellectually disabled populations, despite there being a great need for such strategies. To address this notable gap in the literature, the current article reviews the contemporary literature on forensic interviewing to identify the best methods for questioning children with intellectual disabilities in maltreatment cases. Adaptations to the commonly used forensic interviewing techniques, including verbal, nonverbal, and repeated questioning strategies, are proposed that address the unique developmental, social, and emotional needs of this population. Furthermore, a series of recommendations are provided to enhance the limited forensic interviewing research with this population.
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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 | 0.096 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".