White paper on forensic child interviewing: research-based recommendations by the European Association of Psychology and Law
Bibliographic record
Abstract
This white paper consists of evidence-based recommendations for conducting forensic interviews with children. The recommendations are jointly drafted by researchers in child interviewing active within the European Association of Psychology and Law and are focused on cases in which children are interviewed in forensic settings, in particular within investigations of child sexual and/or physical abuse. One particular purpose of the white paper is to assist the growing Barnahus movement in Europe to develop investigative practise that is science-based. The key recommendations entail the expertise required by interviewers, how interviews should be conducted and how interviewers should be trained. Interviewers are advised to use evidence-based interview protocols, engage in hypothesis-testing and record their interviews. The need to prepare the interview well and making efforts to familiarise the child with the interview situation and create rapport as well as acknowledging cultural factors and the possible need for interpretation is underscored, and a recommendation is made not to rely on dolls, body diagrams and the interpretation of drawings in the interviews. Online child interviewing is noted as showing promising results, but more research is warranted before conclusive recommendations can be made. Interviewers should receive specialised training and continuous feedback on their interviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".