The fear of letting go and the Ivory Tower of dental educational training
Bibliographic record
Abstract
ISSUE: Clinical training in dental education is complex and happens mostly within a well-controlled environment such as a university dental clinic where oral health care services are delivered; it is mostly student-centered. While such training is important, it is also possible to augment and enhance it by training predoctoral dental students outside such a clinic within off-site community-based placements using a more person-centered approach. However, there seems to exist a reluctance in recognizing and utilizing the work produced in these off-site placements holistically as an integral part of students' clinical assessment. APPROACH: Community-based clinical experience adds value to the training of our predoctoral dental students. This perspective describes the benefits of community placements and recognizes their importance in the clinical and professional development of a future graduate. It also presents a way to assess students' performance that by-and-large mirrors that of the university dental clinic while striking a balance between student-centered education and person-centered care. IMPACT: In this perspective, we argue that the clinical work delivered at a community placement ought to be weighted equitably with the clinical work delivered at a university clinic when assessing students' competency as a whole. Our message is to keep a balance of student-centered education and person-centered care to the benefit of all those involved.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".