Bibliometric mapping of the landscape and structure of nutrition and depression research: visualization analysis
Bibliographic record
Abstract
BACKGROUND: Numerous epidemiological studies have examined the relationship between dietary intake of specific foods or nutrients and the incidence of depression and have noted that nutrition has a significant impact on mental health. Therefore, the purpose of this study is to assess the state of research, the frontiers of research, and development trends in the field of nutrition and depression using bibliometric and visual analysis. METHODS: We collected publications on the topic of nutrition and depression from Scopus between 2002 and 2021. Subsequently, we utilized VOSviewer 1.6.18 and Microsoft Excel 2013 to perform bibliometric analysis and visualization. Bibliometric analysis involves retrieving documents from a singular database, such as SciVerse Scopus or Web of Knowledge, and subjecting them to quantitative and qualitative analysis. Notably, gray literature is not considered in bibliometric analysis. RESULTS: A total of 2171 publications on nutrition and depression were found between 2002 and 2021, namely 1855 (85.44%) original articles, 190 (8.75%) reviews, 38 (1.75%) letters, and 88 (4.05%) other types of publications. The most productive country was found to be the USA (n = 726; 33.44%), followed by Australia (n = 172; 7.92%), the United Kingdom (n = 158; 7.28%), China (n = 132; 6.08%), and Canada (n = 131; 6.03%). The remaining publications were from other countries (n = 852; 39.25%). According to the citation analysis, the retrieved papers were cited on an average of 26.6 times and had an h-index of 105 with 57,781 citations. The most frequent terms on the map include those related to (a) fatty acid links to depression and brain inflammation, (b) depression and eating disorders, and finally, (c) adherence to the Mediterranean diet and risk of depression. CONCLUSIONS: The current study was the first novel bibliometric analysis of nutrition and depression research that used data extracted from Scopus for visualization network mapping. In recent years, the theme "Mediterranean diet adherence and risk of depression" has been identified more frequently, indicating that studies in this field have garnered considerable attention and reflect the most recent scientific advances. Researchers should continue to investigate nutrition and depression, and we believe this study provides significant information for researchers, nutritionists, and clinicians.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.119 | 0.123 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| 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".