Interprofessional Education in U.S. and Canadian Dental Schools: An ADEA Team Study Group Report
Bibliographic record
Abstract
The state of interprofessional education (IPE) in U.S. and Canadian dental schools was studied by the American Dental Education Association (ADEA) Team Study Group on Interprofessional Education. The study group reviewed the pertinent IPE literature, examined IPE competencies for dental students, surveyed U.S. and Canadian dental schools to determine the current and planned status of IPE activities, and identified best practices. Members of the study group prepared case studies of the exemplary IPE programs of six dental schools, based on information provided by those schools; representatives from each school then reviewed and approved its case study. Six reviewers critiqued a draft of the study group's report, and study group members and reviewers met together to prepare recommendations for schools. This report identifies four domains of competence for student achievement in IPE and summarizes responses to the survey (which had an 86 percent response rate). It also includes the case descriptions of six schools' IPE programs and the study group's recommendations for dental schools. The report concludes that there is general recognition of the goals of IPE across U.S. and Canadian dental schools, but a wide range of progress in IPE on the various campuses. Challenges to the further development of IPE are discussed.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".