Police interviews with adult reporters of historical child sexual abuse: Exploring the link between verbal rapport and information obtained
Bibliographic record
Abstract
BACKGROUND: Police officers and academics agree that rapport is important when interviewing victims and witnesses, although previous research has found that officers do not always engage in rapport-building behaviours during interviews. Interviews with complainants reporting historical child sexual abuse may be key to police investigations, as physical or corroborating evidence is often not available. OBJECTIVE: This research explored the possible effect of verbal rapport-building behaviour on the elicitation of investigation-relevant details in historical child sexual abuse victim interviews. PARTICIPANTS AND SETTING: A sample of interviews (N = 44) with adults reporting historical child sexual abuse in a northern Canadian territory with a large Indigenous population was examined. METHODS: Interviews were evaluated for interviewer verbal rapport-building behaviours, using a framework derived from Tickle-Degnen and Rosenthal's three domain model of rapport. Interviews were also coded for details given by the interviewee. RESULTS: Results showed that verbal rapport was significantly positively correlated with both total details (r = 0.621, p < .001) and abuse relevant details (r = 0.518, p < .001). Chronological Rapport Maps were piloted, to show the use of rapport behaviours over the course of interviews, and the possible effect over time of these behaviours on information yield. CONCLUSIONS: The results show that information yield is higher when more rapport behaviours are demonstrated and both parties work together harmoniously, even after a long delay. Further research is needed on the experience of police interviews for Indigenous complainants.
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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 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".