Predoctoral Clinical Curriculum Models at U.S. and Canadian Dental Schools
Bibliographic record
Abstract
In fall 2002, the ADEA Section on Comprehensive Care and General Dentistry conducted a survey of the predoctoral clinical curriculum models at sixty-four North American dental schools. Fifty-eight percent of the schools reported that most patient care is provided in a comprehensive care clinic setting, 22 percent reported that most patient care is provided in discipline-specific settings, and 20 percent reported a hybrid of comprehensive care and discipline-specific settings. While ten Primarily Discipline-Based (PD) schools have instituted new Primarily Comprehensive Care (PCC) or Hybrid clinical curricula since 1997, one PCC school has converted to a Hybrid model, and one PCC school has converted to a PD model. PCC curriculum models were frequently associated with the following institutional factors: more densely populated metropolitan areas; private institutional sponsorship; location within a university medical center; larger class size; and more students enrolled in advanced training at the school. Curriculum factors frequently associated with PCC models included the following: increased use of simulation technology: higher proportion of clinical/teaching track faculty; higher proportion of part-time faculty; higher proportion of generalist faculty; same faculty supervising both treatment planning and patient treatment; and use of competency exams as the main requirement for completion of the curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".