Teaching approaches in South African dental schools: direct restorative procedures.
Bibliographic record
Abstract
INTRODUCTION: Worldwide the use of amalgam has declined and mercury-containing products are banned in several countries. National and international opinions on amalgam were recently discussed in journals. According to surveys, significant time is spent on the teaching of amalgam in American, Canadian, Irish and United Kingdom Dental Schools. AIMS AND OBJECTIVES: To i) investigate the teaching approaches on direct restorative techniques and materials in South African Dental Schools; ii) compare the teaching approaches of the dental schools in South Africa with each other as well as with the American, Canadian, Irish and United Kingdom schools; iii) use the information of this study as baseline data for future studies on teaching approaches. METHODS: A questionnaire regarding the teaching and training of direct restorations was e-mailed to the heads of Restorative Dentistry departments in four South African Dental Schools in 2007. RESULTS: Significant time is spent on teaching and training of amalgam as a restorative material. Teaching and training on direct restorations are very similar in all South African Dental Schools. CONCLUSION: Although there is a decline in the use of amalgam worldwide, significant time is spent on teaching of amalgam restorations in South African Dental Schools and this corresponds to the curriculums of American, Canadian, Ireland and United Kingdom Dental Schools.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".