The ethics of child maltreatment surveys in relation to participant distress: Implications of social science evidence, ethical guidelines, and law
Bibliographic record
Abstract
Epidemiological surveys measuring the prevalence of child maltreatment generate essential knowledge that is required to enhance human rights, promote gender equality, and reduce child abuse and neglect and its effects. Yet, evidence suggests Institutional Review Boards (IRBs) may assess the risk of these studies using higher than normal thresholds, based on a perception they may cause high distress to participants. It is essential for IRBs and researchers to have an accurate understanding of the nature and extent of participant distress associated with these studies, and of the duties of researchers towards survey participants, so that meritorious research is endorsed and duties to participants discharged. Assessment by IRBs of the ethics of such research must be appropriately informed by scientific evidence, ethical principles, and legal requirements. This article adds to knowledge by considering participant distress in child maltreatment surveys and its appropriate ethical and operational treatment. We provide an updated overview of scientific evidence of the frequency and severity of distress in studies of child maltreatment, a review of ethical requirements including a focus on beneficence and participant welfare, and a new analysis of researchers' legal duties towards participants. Our analyses demonstrate that participant distress is infrequent and transitory, that researchers can satisfy ethical requirements towards participants, and that legal liability does not extend to emotional distress. Informed by these bodies of knowledge, we distil key principles of good epidemiological practice to provide solutions to operational requirements in these surveys, which both fulfil ethical requirements to participants, and demonstrate trauma-informed practice.
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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 | 0.728 | 0.802 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.095 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".