Research in PBL - where to from here for dentistry?
Bibliographic record
Abstract
Brief history of PBL in dental educationProblem-based learning (PBL) was first introduced into medical education at McMaster University in Canada in the 1960s and it then spread to many other medical schools.Its introduction into dental education occurred much later, with schools in Sweden, Australia, USA, Hong Kong, Ireland and the United Kingdom implementing PBL-based curricula in the 1990s.Since then, PBL has been introduced into many dental programmes around the world.In most cases, rather than full PBL programmes, hybrid programmes have been developed or PBL has been introduced into one or more courses, alongside other more traditionally presented courses.Since its introduction, PBL has been a controversial topic.Those who first embraced the approach generally became strong supporters, while others remained sceptical.Most of the research that has been carried out on PBL is reported in the medical education literature, although there have been a few studies relating to dentistry that have been published, mainly in the European Journal of Dental Education (EJDE) and the Journal of Dental Education.(JDE).The main topics covered in these publications will be summarised later in this paper.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".