Global Trends and Future Prospects of Child Nutrition: A Bibliometric Analysis of Highly Cited Papers
Bibliographic record
Abstract
Child nutrition has always been a global concern. This study performed visual analysis of 1,398 child nutrition highly cited papers (HCPs) from 2009 to 2019. The purpose of the study was to evaluate and present the performances of authors, journals, countries, institutions, top cited papers; to explore the hot topics, prospects, and to propose the future research directions on child nutrition. We used bibliometric methods to conduct in-depth statistical analysis of HCPs on child nutrition, showing research progress, trends and hot spots. We included HCPs on child nutrition from the Science Citation Index-Expanded (SCI-E) database February 7, 2020. Two tools, CiteSpace and VOSviewer, were used to conduct the bibliometric analyses. The results showed that, since 2011, the number of HCPs on child nutrition has increased rapidly. The top three contributors in this field were the USA, the UK and Canada. However, the contribution of developing countries was very limited. Intestinal microflora, food allergy, overweight and obesity were the three major research hotspots in this field. Results of this study provide valuable references for ongoing child nutrition related research, which may be interesting and noteworthy to the researchers involved.
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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: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.098 | 0.120 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".