A Nationwide Survey of Child Interviewing Practices in Canada
Bibliographic record
Abstract
The goal of the present study was to create professional awareness about the degree of consensus and consistency in the interview techniques that ultimately influence child victims' experiences and progression through the legal system in Canada. We surveyed 200 professionals who interview children in Canada about the guidelines and techniques they use, their perceptions of their training and interviewing arrangements, and the needs and challenges they face in daily practice. Results revealed a wide variety of practices in use across the country, and differences in length of training and who provided it. Police and child protection workers tended to differ on their satisfaction with interviewing arrangements. Commonalities were observed across organizations and locales in that most interviewers could readily identify challenges in talking to vulnerable witnesses and desires for additional training (e.g., greater topic breadth and regular follow-ups). Responses revealed awareness of contemporary research, as well as pockets of more traditional thinking, about child witness capabilities and interviewing techniques. Although variety in interviewing guidelines and training providers is not necessarily problematic, the development of a single nationwide policy on the core components of vulnerable witness interviewing, to which training programs must adhere, could have particular value.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".