Research on Psychache in Suicidal Population: A Bibliometric and Visual Analysis of Papers Published During 1994–2020
Bibliographic record
Abstract
Background: Psychache is a negative introspective experience, which is positively associated with the risk of suicide, independently of depression. It is undeniable that psychache is an important influencing factor to trigger suicide, which can also mediate the effect between depression and suicide variables. Nevertheless, the research tendency and current hotspots on psychache of suicide population have not been systematically investigated based on bibliometric analysis. Aim: The aim of the study was to analyze the research status, hotspots, and frontiers of psychological pain in the field of suicidology, so as to provide reference for domestic clinical research. Methods: The literature related to psychache in suicide individuals published from 1994 to 2020 was included and selected from the Web of Science Core Collection database on May 28, 2021. CiteSpace (version 5.7.R2) software was used to visualize and analyze highly cited journals, authors, and articles as well as co-occurrence analysis for countries, institution, authors, and keywords. Results: A total of 230 articles from the WoS database were included. The number of papers over the years showed an overall upward trend. The United States has made the largest contribution, with the majority of publications (89, 38.70%), followed by Canada (48, 20.87%), Israel (31, 13.48%), China (20, 8.80%), and Portugal (17, 7.39%). The most productive institution was Queen's University. Edwin S. Shneidman has the largest achievement and profound influence, and the most prolific author is Ronald R. Holden. However, the cooperation between institutions and authors was comparatively weak. The current hotspots in this field focus on the studies on the relationships between depression, despair, psychache, and suicide, the risk assessments of psychological pain, and the development of psychological pain scales. Suicide and Life-Threatening Behavior was the most frequently cited journal in this field. Conclusions: This analysis not only reveals the current research trend and hotspots but also provides some instructive suggestions on the development of psychache in the suicidology. Future work should pay more attention to develop effective psychological pain intervention programs for diverse suicide population. Additionally, longitudinal study can also be conducted to grasp the trajectory changes of psychological pain among suicide individuals.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.218 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".