Areas of Interest in Dental Education: A Bibliometric Analysis of the Last Decade
Bibliographic record
Abstract
This study aimed to perform a comprehensive bibliometric analysis of journals focused on dental education (Journal of Dental Education and European Journal of Dental Education) from 2014 to 2023. An ISI Web of Science Search was performed in October 2023 with no filters for language or keywords. Published articles between 2014 and 2018, 2019 and 2023, and 2014-2023, along with the top 100 cited articles published within this period were exported as txt files. Keyword and title word network maps and occurrences were generated using VOS Viewer software. Author-affiliated countries with the most publications were tabulated from the Web of Science. Dental education and dental students and education were consistently in the top six keywords and title word occurrences in all periods and top 100 cited articles. Similar trends were observed for keyword and title word network maps with an emphasis on dental education and students. However, the 2019-2023 period saw the emergence of coronavirus disease 2019, three-dimensional printing, virtual reality, and education technology, with the earlier period (2014-2018) showing clusters around students, perceptions, dental hygiene education, and assessment. The United States ranked top of the list for most published author-affiliated countries, with England, Canada, Australia, and Saudi Arabia in the top six for all periods analyzed. In conclusion, within the limitations of this study, areas of interest in dental education journals in the last decade were identified along with the countries with most publications.
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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: Empirical 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.025 | 0.138 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".