A Study of the Relationship between Investigators’ Personal Characteristics and Adherence to Interview Best Practices in Training
Bibliographic record
Abstract
In the past 20 years, guidelines have been developed to help interviewers investigate cases of alleged child sexual abuse (CSA), but few studies have assessed investigators’ abilities to adhere to these guidelines. This study aimed to evaluate whether some investigative interviewers’ personal attributes are related to their performance in a mock investigative interview with a child victim of sexual abuse. Assessment was conducted in a police training context. A total of 24 French Canadian police investigators followed a one-week training program aimed at using the National Institute of Child Health and Human Development (NICHD) interview protocol. These investigators performed a mock interview at the end of the training to test their practical competence. They also filled out three questionnaires assessing their cognitive abilities, emotional intelligence, and personality traits. Interview performance was positively related to cognitive abilities, female gender, and the personality trait of Openness to Experience, and negatively related to stress management and level of experience. Four regression models are presented to explain interview performance variance. The results of this study show that some personal characteristics, such as cognitive abilities, personality, gender and experience, affect investigative interviewers’ performance with a child victim of sexual abuse in a police training context.
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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 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".