Interviewing Indigenous adults reporting historical child sexual abuse: The effect of question types on eliciting descriptive answers and details
Bibliographic record
Abstract
BACKGROUND: International evidence-based best practice for police interviewers of vulnerable groups, such as Indigenous populations, recommends encouraging interviewees to give a full uninterrupted account, followed by open-ended questions, to optimise memory and avoid contaminating information. However, most research examining the applicability of interview strategies on information gain has been conducted in western cultures. OBJECTIVE: There is currently little extant quantitative research on questioning in police interviews with Indigenous complainants. The primary objective of this research was to examine whether international standards for interviewing vulnerable groups for legal purposes are transferable to an Indigenous population. PARTICIPANTS AND SETTING: Police interviews with complainants reporting historical childhood sexual abuse [HCSA] as adults in a northern Canadian territory with an Indigenous population (N = 45 interviews) were examined. METHODS: Interviews were coded for types of questions, answers, and investigation-relevant details reported. Frequency distributions were calculated for each dependent variable, and further inferential statistics were conducted using t-test, chi square, and one-way ANOVA analyses, to examine the possible effect of question types on the elicitation of certain answer and detail types. RESULTS: Results showed a statistically significant difference in the mean number of overall details elicited (d = 0.29), with questions classed as productive eliciting more details compared to unproductive questions. Specifically, open-ended questions elicited the most details, including both overall details and abuse relevant details. CONCLUSIONS: Although these results should be considered exploratory, the international guidance on interviewing vulnerable groups was found to be applicable to this Indigenous population.
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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 | 0.098 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".