The Use of Technologies for Teaching Dentistry in Brazil: Reflections from an Integrative Review
Bibliographic record
Abstract
This article is an integrative review regarding the use of information and communication technologies (ICT) for teaching Dentistry. Thus, the article aimed to analyze papers that show the use of these technologies as resources and tools for learning. The stages in the elaboration of this integrative review were: establishing the guiding question and aims of the study, establishing the inclusion and exclusion criteria for articles, defining the research instrument and information to be extracted from the articles selected, results analysis, and discussion. For this, bibliographical data was collected from the SciELO, ColecionaSUS, and Periódicos CAPES databases in the search for articles published in the last five years, written in Portuguese, and containing the following keywords in Health Sciences: “education in dentistry”, “dentistry”, “dentistry informatics”, “distance learning”, and “education”, and which were related in context to the topic of study. Eleven articles were selected as the results, which were analyzed using the data collection instrument. It was concluded that the current technologies used as teaching resources and tools contribute as allies for improving ways of teaching and learning, particularly in the area of dentistry, in a way that makes courses more interactive and dynamic, and adding personal and technical skills to the profiles of the professionals trained.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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, 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".