Public concern of child maltreatment in China from 2011 to 2024 and its socioeconomic determinants: An Infodemiology analysis using Baidu Index
Bibliographic record
Abstract
Child maltreatment represents a significant public health issue affecting children's well-being. In China, traditional cultural beliefs and social factors contribute to substantial underreporting and underestimation of maltreatment cases. Infodemiological approaches offer new perspectives for examining this hidden social phenomenon. This study aims to analyze the temporal evolution of public concern about child maltreatment in China, examine its socioeconomic determinants, and forecast future trends. I utilized the Baidu Index to obtain search volumes for child maltreatment-related keywords from 2011 to 2024, which were then integrated with provincial-level socioeconomic indicators across China. Structural breakpoint analysis, STL decomposition and ARIMA modeling were employed to analyze temporal patterns and generate forecasts. Standardized regression analysis was used to explore the associations between socioeconomic factors and public concern. Public concern about child maltreatment exhibited four distinct phases: rapid increase (2011–2014), stable (2014–2018), slow decrease (2018–2022), and accelerated decrease (2022–present). Forecasts indicate a continuing decline next two years. Urbanization rate ( β = 0.59, p < .001) and fertility rate ( β = 0.36, p < .001) demonstrated significant positive associations with public concern, while sex ratio showed negative correlation ( β = − 0.07 , p < .001). Significant regional disparities were evident, with eastern regions maintaining consistently higher levels of concern while western regions showed a marked decline. The findings reveal that public concern about child maltreatment in China is currently in a declining cycle, especially in western regions. The temporal and spatial pattern correlates closely with levels of social development and demographic structure. A more comprehensive public concern monitoring system needs to be established. • First large-scale infodemiology study examining child maltreatment public concern in China. • Identified four distinct phases in public concernt: rapid increase , stable , slow decrease , and accelerated decrease . • Revealed significant regional disparities in public concern, with western provinces showing consistently lower concern. • Urbanization, internet, and fertility rates were positively associated with public concern, while gender ratio was negative. • Found evidence of compassion fatigue in child maltreatment issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".