Analyzing ethical dimensions of mental disorders: trends and key research areas through bibliometric methods
Bibliographic record
Abstract
This study aimed to explore key ethical issues related to mental disorders through a bibliometric and cluster-based content analysis of existing academic literature. A comprehensive literature search was conducted in the Scopus database (Elsevier) up to December 31, 2024, using ethics-and mental disorder-related keywords. The search was limited to English-language journal articles in medicine, psychology, neuroscience, and other related fields. After title and abstract screening, 1,271 articles were included (κ=0.907). Bibliometric analyses including keyword co-occurrence, citation coupling, and country/author mapping were performed using VOSviewer (Centre for Science and Technology Studies, Leiden University) and Gephi (Gephi Consortium). A cluster-based content analysis was used to interpret the thematic structure of the field. The annual publication volume showed an upward fluctuating trend, with increasing scholarly attention post-1994. Coauthor networks revealed weak centralization, and the core author group remained underdeveloped. Research has been geographically concentrated in North America and Western Europe, particularly in the United States, the United Kingdom, and Canada. Keyword analysis identified six major thematic clusters: (1) conceptual foundations and policy frameworks in mental health ethics, (2) ethical challenges in psychiatric care, (3) research ethics, (4) patient autonomy and rights, (5) end-of-life decision-making and palliative ethics, and (6) neuroethics and emerging biomedical technologies. Recent popular topics include artificial intelligence, epistemic injustice, and medical aid for the dying. This study maps the intellectual structure and evolving focus of the ethical discourse on mental health. These findings highlight the need for ethically responsive frameworks that address patient autonomy, technological advancement, and global equity.
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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 | 0.031 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.278 | 0.341 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| 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".