How anatomy is taught to dental students: Results of an ADEA survey to US and Canadian faculty
Bibliographic record
Abstract
Members of the Anatomical Sciences Section of the American Dental Education Association (ADEA) recently completed an international survey of the directors of all North American dental gross anatomy courses to assess: 1) what level of detail are specific topics and content areas being presented; 2) when in the curriculum are specific content areas are taught; 3) what areas are omitted; and 4) what other areas are of concern with regard to North American dental gross anatomists. The survey received a 95.5% response rate representing 64 (of 67) of the US and Canadian dental schools. The results of this survey indicate, amongst other things that: 1) the use of computer‐assisted instruction (CAI) tools has increased; 2) emphasis on clinical topics has increased; 3) reliance upon medical school faculty and facilities is high; 4) a pattern of increased use of integrated curricula among dental schools has emerged; and 5) a general trend for a decrease in student contact hours is ongoing. The specific data are currently being analyzed and will be presented. These data will provide the framework by which course directors and administrators, in need of curricular information, can make more informed decisions in the appropriateness of content and even provide guidance in evaluation of their program. Grant Funding Source AAA Young Faculty Travel Award – Supported by AAA Second Century Fund
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".