Mapping Two Decades of Childhood Emotional Abuse Research: A Global Bibliometric Analysis (2005–2024)
Bibliographic record
Abstract
Objective: Child emotional abuse (CEA) is associated with a wide range of detrimental consequences, both in childhood and adulthood. Despite its widespread prevalence and long-term impact, it has historically received less scholarly attention compared to physical and sexual abuse. To address this gap, his study presents the first global bibliometric analysis of CEA research from 2005–2024, mapping its evolution, thematic trends, and geographical distribution. Method: Using Scopus and PubMed, 1,040 articles and reviews in English were analyzed via the R-based Bibliometrix package. Descriptive, network, and thematic analyses identified publication patterns, collaboration networks, and conceptual trends. Results: Publications on CEA have risen sharply since 2018, with psychology, medicine, and psychiatry dominating the field. The United States, China, and Canada are the most productive countries, while many Low- and Middle-Income Countries (LMICs) remain underrepresented (e.g., Iran 0.96%). Trend analyses reveal a thematic shift from immediate psychological distress toward developmental mechanisms, transdiagnostic constructs (e.g., early maladaptive schemas), and culturally contextual factors. Thematic mapping shows underdeveloped core areas (e.g., depression, child trauma), well-developed motor themes (e.g., early maladaptive schemas, meta-analysis), and niche/emerging topics (e.g., fMRI, gene–environment interaction). Conclusion: CEA research is expanding toward integrative, culturally informed, and mechanism-focused frameworks, but definitional, methodological, and geographical gaps persist. Targeted investment in LMIC research, validated and culturally adapted tools, interdisciplinary collaboration, and prevention-oriented strategies are urgently needed.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.209 | 0.246 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".