A meta-analysis of differences in children’s reports of single and repeated events.
Bibliographic record
Abstract
When children report abuse, they often report that it occurred repeatedly. In most jurisdictions, children will be asked to report each instance of abuse with as many details as possible. In the current meta-analysis, we analyzed data from 31 experiments and 3099 children. When accuracy was defined as the number of correct details from the target instance (i.e., narrow definition), repeated-event children were less accurate than single-event children. However, we argue that defining accuracy as the number of reported details that were experienced across instances (i.e., broad definition) is more appropriate for repeated events. When a broad definition was applied, single- and repeated-event children were similarly accurate. Importantly, repeated-event children were less likely than single-event children to report details that had never been experienced and they were no more likely to say "I don't know." Overall, repeated-event children were more suggestible than single-event children, but this was moderated by length of delay to recall. In analyses of recognition data, single-event children's sensitivity score was higher than repeated-event children's, with no significant difference in response bias as a function of event frequency. We discuss these results in the context of how children's memory for repeated events is organized. We also consider the advantage of applying a broad definition of accuracy for victims of repeated abuse and charging repeated abuse as a continuous offense rather than discrete acts. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".