What type of survey research questions are identified by adults as upsetting? A focus on child maltreatment
Bibliographic record
Abstract
BACKGROUND: Research on child maltreatment is imperative to inform evidence-based prevention and intervention efforts. Nonetheless, researchers continue to face barriers due to the perceived sensitivity and possibility of harm when asking about these experiences. While studies have started to explore reactions to participating in research on sensitive topics, there are notable limitations and fewer have focused on child maltreatment. OBJECTIVE: The objective of this study was to better understand adult respondents' identification of, and reactions to, potentially upsetting questions in the context of a well-being and experiences survey, with a focus on child maltreatment. METHODS: Data were from the first wave of the Well-Being and Experiences Study in Manitoba, Canada: a computerized self-reported community-based survey of adolescents and their parents/caregivers administered individually at a research facility. The current study focused on parents/caregivers' responses (N = 1000). The study utilized a mixed methods approach with descriptive statistics and qualitative thematic analyses of open-ended responses of their perceptions of upsetting questions. RESULTS: Overall, few respondents (15.1 %) identified any questions as upsetting. Ten themes emerged in respondents' recall of upsetting questions, including maltreatment and other themes often perceived as less sensitive. Only 4% identified maltreatment-related questions as upsetting. Among those who identified any questions or maltreatment-specific questions as upsetting, most felt they were important to ask and should not be removed (92.7 %-97.5 %). These findings suggest that retrospective survey questions about experiences of child maltreatment involving adult samples are not associated with major upset and should be included in future health and social surveys.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".