Research status, hotspots, and implications of seasonal affective disorder: A bibliometric analysis based on CiteSpace and VOSviewer
Bibliographic record
Abstract
The objective was to determine the research status and hotspots of seasonal affective disorders (SAD) based on bibliometric tools, which will contribute to the further research in this field. We used bibliometric tools CiteSpace and VOSviewer to conduct visual quantitative analysis on 465 SAD literatures in the Web of Science core database from 2008 to 2023 from multiple perspectives such as collaboration network, keywords, and literature citations. At the same time, we used Microsoft Word to make relevant tables. The publication of SAD-related literature has been on the rise in the past 15 years, countries with high production of SAD literature are mainly concentrated in the United States, Austria, and Canada, and certain cooperative relationships have been established between various institutions and scholars. Research keywords in our study are mainly limited to pathogenesis ("Photoperiod," "exposure," "winter," "serotonin transporter," and "creb") and treatment measures ("light therapy" and "melatonin"). In recent years, literature research hotspots mainly focus on the treatment of SAD with light therapy, the application of exogenous drugs, the biological clock mechanism of SAD pathogenesis, the relationship between SAD and inflammation, etc. The correlation between SAD and sleepiness and alternative treatments to light therapy may be future research hotspots. The research results reveal the future research focus of SAD. There is a considerable interest in the photoperiodic pathogenesis of SAD, light therapy and its alternative therapies, and there is still hope for further exploration. Substantial research into evidence-based prevention as well as treatment strategies is necessary to improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.304 | 0.330 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".