Don’t know responding in young maltreated children: The effects of wh- questions type and enhanced interview instructions.
Bibliographic record
Abstract
OBJECTIVE: Two studies examined 4-7-year-old maltreated children's "I don't know" (IDK) responses to wh- questions after receiving various interview instructions. HYPOTHESES: We predicted (H1) children would be less inclined to give IDK responses and more inclined to guess to color/number questions compared to other wh- questions; (H2) IDK instructions would increase children's IDK responding compared to no instructions, with an increase in accuracy; but (H3) instructions would be less effective in reducing guessing for color/number questions than other wh- questions. In Study 1, we predicted that (H4) verbalizing a commitment to answer IDK would be particularly effective. In Study 2, we predicted that (H5) IDK instructions would reduce children's accurate corrective responses, but that (H6) the negative effect of IDK instructions on corrective responses would be alleviated by a "correct the interviewer" instruction. METHOD: Across 2 studies, 301 four- to seven-year-old (M = 5.60, SD = 1.09) maltreated children viewed videos and answered wh- questions about true and false details. Both studies included a within-subjects manipulation of wh- types (color/number & wh- detail) and a between-subjects manipulation of instructions (Study 1: IDK practice, IDK practice/verbalize, control; Study 2: IDK, correct me, IDK + correct me, control). RESULTS: In both studies, (a) color/number questions elicited more guessing than wh- detail questions, (b) IDK instructions decreased inaccurate responses, but they also decreased accurate responses, including accurate corrective responses, and (c) IDK instructions had a larger effect on wh- detail questions, reducing accurate corrective responses. In Study 1, verbalization failed to enhance the effect of instructions. In Study 2, the negative effect of IDK instructions on accurate corrective responses was not alleviated by instructions to correct the interviewer. CONCLUSIONS: Among young maltreated children, color/number questions elicit higher rates of guessing than other wh- questions. IDK instructions reduced inaccurate responses, but also reduced accurate responses. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".