A Bibliometric Analysis of the Global Research Trend in Child Maltreatment
Bibliographic record
Abstract
Child maltreatment remains a major health threat globally that requires the understanding of socioeconomic and cultural contexts to craft effective interventions. However, little is known about research agendas globally and the development of knowledge-producing networks in this field of study. This study aims to explore the bibliometric overview on child maltreatment publications to understand their growth from 1916 to 2018. Data from the Web of Science Core Collection were collected in May 2018. Only research articles and reviews written in the English language were included, with no restrictions by publication date. We analyzed publication years, number of papers, journals, authors, keywords and countries, and presented the countries collaboration and co-occurrence keywords analysis. From 1916 to 2018, 47,090 papers (53.0% in 2010⁻2018) were published in 9442 journals. Child Abuse & Neglect (2576 papers; 5.5%); Children and Youth Services Review (1130 papers; 2.4%) and Pediatrics (793 papers, 1.7%) published the most papers. The most common research areas were Psychology (16,049 papers, 34.1%), Family Studies (8225 papers, 17.5%), and Social Work (7367 papers, 15.6%). Among 192 countries with research publications, the most prolific countries were the United States (26,367 papers), England (4676 papers), Canada (3282 papers) and Australia (2664 papers). We identified 17 authors who had more than 60 scientific items. The most cited papers (with at least 600 citations) were published in 29 journals, headed by the Journal of the American Medical Association (JAMA) (7 papers) and the Lancet (5 papers). This overview of global research in child maltreatment indicated an increasing trend in this topic, with the world’s leading centers located in the Western countries led by the United States. We called for interdisciplinary research approaches to evaluating and intervening on child maltreatment, with a focus on low-middle income countries (LMICs) settings and specific contexts.
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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 | 0.018 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.233 | 0.321 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".