Assessing the efficacy of investigative interviewing training courses: A systematic review
Bibliographic record
Abstract
Substantial resources have been dedicated to designing and implementing training courses that focus on enhancing the interviewing skills of police officers. Laboratory research studies and real-world assessments of the effectiveness of interview training courses, however, have found notably mixed results. In this article, empirical studies ( N = 30) that have assessed the effectiveness of police interview and interrogation training courses were systematically reviewed. We found a wide variation in terms of the type, length, and content of the training courses, the performance criteria used to assess the training effectiveness, and the impact of the training courses on interviewing performance. Overall, the studies found that basic interviewing skills can be developed to a certain level through even short evidence-based training courses. More cognitively demanding skills, such as question selection and meaningful rapport-building, showed less of an improvement post training. The courses that included multiple training sessions showed the most consistent impact on interviewing behavior. This review also indicated a need for more systematic research on training effectiveness with more uniform and longer-term measures of effectiveness. Our findings should help guide future research on this specific topic and inform the training strategies of law enforcement and other investigatory organizations.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".