Factors Associated with Social Workers’ Likelihood to Report Suspected Child Abuse: Findings from a National Survey of Chinese Social Workers
Bibliographic record
Abstract
Abstract Whilst there have been recent national surveys about reporting child abuse in several low- and middle-income countries, data from China are lacking. This study examined the prevalence of reporting suspected child abuse and factors associated with having ever reported in a sample of social workers from mainland China. Nationally representative data from the China Social Work Longitudinal Study were used, and responses from 3,510 participants who had completed the children’s social work questionnaire module were analysed. Our results indicate that only 14.4 per cent of social workers from China had ever reported suspected child abuse. Participants who had ever reported were more likely to be female, younger, have higher education levels and social work qualifications, have a higher awareness of abusive behaviours, and have greater working autonomy than social workers who had never reported. We argue that in order to achieve higher levels of reporting suspected child abuse for Chinese social workers, it is necessary to promote the professionalisation of social work through both education and experience gained in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".