Forty Years of Knowledge Development on Emotional Abuse and Suicide – AI-Assisted Bibliometric and Empirical Insights
Bibliographic record
Abstract
This bibliometric study investigates worldwide research patterns on emotional abuse and suicide in 898 papers from 1985 to 2025 using data from the Web of Science Core Collection analysed with VOSviewer to study keyword co-occurrence, thematic clusters, temporal development, and country-level collaboration. Five primary research clusters focusing on suicide, childhood trauma, depression, risk factors among adolescents, and emotion regulation were revealed by the analysis. The USA, Canada, China, and England were found to be core contributors in worldwide co-authorship networks.Additionally, an empirical observational analysis was performed on 50 anonymised inpatients from the Socola Institute of Psychiatry in Iasi, assessing the severity of depression (HAM-D), suicidal ideation (BSS) and clinical follow-up results over a period of nine months.In the empirical arm of this study, which involved 50 anonymised inpatients, depression severity scores (HAM-D) and suicidal ideation (BSS) showed significant reductions from admission to nine-month follow-up (p < 0.001), while residual depressive symptoms at discharge (HAM-D T1) independently predicted persistent suicidality (OR = 1.12, 95% CI 1.02–1.23, p = 0.014). Results point toward the intricate, multidisciplinary nature of the field and the increasing concentration on trauma-informed prevention approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.098 | 0.133 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".