Global research trends in the teacher evaluation of medical college: A bibliometric and visualized study
Bibliographic record
Abstract
Background: The evaluation of education in colleges and universities is not only an essential means to promote teachers' professional development and ensure high-quality development of education but also a strong support for the overall development of colleges and universities. In this study, to understand the development trends and predict the future of teacher evaluation in medical colleges from 2005 to 2023, we conducted a comprehensive bibliometric and visualized study using Web of Science. Methods: The articles on teacher evaluation in medical colleges were extracted from the "Core Collection" of the WoS database. VOSviewer software, CiteSpace and R-Bibliometrix Package were employed to visually analyze countries/regions, journals, authors, keywords, institutions, and highly cited articles in this field. Results: The number of articles on teacher evaluation in medical colleges has gradually increased. The United States has the most significant number of publications in this field. The most frequently used keywords were "medical education," "education," "teaching," "assessment," and "curriculum." BMC Medical Education was the leading journal. The leading institutions were Pontificia Universidad Católica de Chile and the University of Toronto. Furthermore, Singh T, Roberts C, Riquelme A, Raupach T, and Padilla O published the most papers. Conclusion: This study's results indicate that teacher evaluation in medical colleges remains a significant area of research worldwide. The findings will contribute to the ongoing study of teacher evaluation in medical colleges.
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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 | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.080 | 0.144 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".