Adverse childhood experiences (ACEs) research: A bibliometric analysis of publication trends over the first 20 years
Bibliographic record
Abstract
BACKGROUND: The relative health and robustness of a field of research can be approximated by assessing peer reviewed journal publication trends for articles pertinent to the field. To date, there have been no such assessments of the burgeoning research on adverse childhood experiences (ACEs). OBJECTIVE: The overall goal of this study was to examine ACEs research trends using bibliometric methods. More specifically, we sought to describe observed publication trends of the ACEs literature from its inception in the late 1990s. We also analyzed the nature of ACEs publications with regard to key characteristics of main outcomes, levels of analysis, and populations of primary focus. METHODS: A search was conducted using Scopus to identify English language papers on ACEs published in peer-reviewed journals between 1998 and 2018. The primary field of research was determined by having independent raters code the title of the publishing journal into distinct categories. Main research outcomes were similarly coded. RESULTS: A total of 789 articles on ACEs appearing in 351 different academic journals were published between 1998 and 2018. There was considerable growth in the number of ACEs papers published over the past several years. General medicine and multidisciplinary research were the most frequent of 12 primary fields of research characterizing ACEs research. Of 16 primary outcomes on which ACEs research focused, the most common were mental health and physical health. CONCLUSION: Significant growth in ACEs research over the past several years suggest the field is thriving. Observed publication trends and publication characteristics are discussed briefly.
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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 | 0.023 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.176 | 0.198 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| 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".