An analysis of reporting practices in the top 100 cited health and medicine-related bibliometric studies from 2019 to 2021 based on a proposed guidelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bibliometric analysis has gained popularity as a quantitative research methodology to evaluate scholarly productivity and identify trends within specific research areas. However, there are currently no established reporting guidelines for bibliometric studies. The present study aimed to investigate the reporting practices of bibliometric research related to health and medicine based on a guidelines "Preferred Reporting Items for Bibliometric Analysis (PRIBA)" proposed in this study. The Science Citation Index, Expanded of the Web of Science was used to identify the top 100 articles with the highest normalized citation counts per year. The search was conducted on April 9, 2022, using the search topic "bibliometric" and including publications from 2019 to 2021. The results substantiated the need for a standardized reporting guideline for bibliometric research. Specifically, among the 25 proposed items in the PRIBA, only five were consistently reported across all articles examined. Further, 11 items were reported by at least 80% of the articles, while nine items were reported by less than 80% of the articles. In conclusion, our findings suggest that the reporting practices of bibliometric studies in the field of health and medicine are in need of improvement. Future research should be conducted to refine the PRIBA guidelines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.474 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.591 | 0.902 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it