Bibliographic record
Abstract
Skills and development: The way undergraduate dental students have been taught and their methods of learning have evolved over decades. Education methods and needs: Perhaps the most rapid and exponential changes have been in the last two decades with the introduction and utilisation of digital media platforms and social media capabilities. Academic and clinical aspects of dentistry are divided within the curriculum, but less consideration and logic are exercised when focusing on the methods of delivering education and the students’ own preferences, capabilities and adaptation towards learning. Technology and dental education and what we believe: In higher education, closed questionnaires were provided to both dental students (50) and teachers (10) relating to delivery methods and their beliefs regarding education techniques available. Opinions regarding these methods still differ amongst dental teachers and students, with an affinity from the dental students towards the use of emerging technology available in dentistry. However, the questionnaires revealed both groups preferred education via direct care on patients more than any other method of education. Conclusion: The literature would indicate some progress made within the dental profession relating to the use of digital media, advanced technology and improved dental software, however, this has not yet been transferred to dental higher education, despite an accessible and obvious availability of modern resources and techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".