The use of note-taking during forensic interviews: Perceptions and practical recommendations for interviewers
Bibliographic record
Abstract
The disclosure process for children who have experienced maltreatment is often difficult. In an effort to support children in their disclosures, interviewers have increasingly turned to empirically-based interview protocols (i.e., questioning strategies) that both decrease the suggestibility of questions while also increasing the productivity of children’s statements. Despite efforts to improve the structure of forensic interviews, interviewing support tools, such as note-taking, have received less empirical attention. To date, research examining interviewers’ notes has primarily focused on the accuracy of such records for evidentiary reasons. Yet, note-taking may serve other purposes; for instance, the process of note-taking may increase the accuracy of interviewers’ questions (i.e., use of child’s words) and memory (i.e., follow-up questions and themes) throughout the interview. In the current review, we describe the limited forensic note-taking literature, as well as the potential strengths and weaknesses of note-taking during forensic interviews with children. We end by suggesting potential avenues of research to assist with the creation of practical guidelines for the use of notes during forensic interviews.
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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 | 0.348 | 0.399 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.008 | 0.013 |
| 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".