Thinking ecologically about clinical education in dentistry
Bibliographic record
Abstract
Traditional approaches to clinical education (CE) in dentistry have primarily focused on the needs and interests of students (student-centred), patients (patient-centred) or individuals receiving care (person-centred). Research has shown that giving priority to the interests of one stakeholder (eg students) may negatively affect the interests of others (eg patients, instructors and administrators). In this commentary, we discuss some limitations of traditional approaches to CE and suggest an eco-centred approach that assumes that the interests of all stakeholders must be considered when planning CE due to the interdependent relationships between stakeholders. A description of this new approach is provided, whilst research and innovation are encouraged to develop an ecologically informed system of CE.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".