Measuring Clinical Productivity in Community‐Based Dental Education Programs
Bibliographic record
Abstract
Community-based dental education programs (CBDEPs) are an important part of the curriculum in many dental schools. This article describes the redesign of the CBDEP at the University of Kentucky College of Dentistry. As part of the formative evaluation of this CBDEP, information on clinical productivity was collected in order to educate intramural faculty members about the scope and extent of services provided in extramural sites; develop an understanding of the productivity expected; complement the intramural education by placing dental students in particular settings where they could enhance certain clinical disciplines; and serve as a decision making tool in the selection of sites that provide the richest clinical experiences. A total of 158 students participated in the CBDEP during the three years of our study (2006-08). Productivity per site was calculated based on total, mean, and median number of services provided, billing (utilizing Kentucky's Medicaid fees for 2006), and Relative Value Units (RVUs). A total of 26,202 services, $972,109 in billing, and 43,053 RVUs was generated by the students, with an average of 175 services, $6,481, and 287 RVUs per student. Four categories (restorative, oral surgery, preventive, and diagnostic) accounted for 88 percent of total RVUs for all sites and all years. Productivity measured with RVU was the highest at private practices. Students spent most of their time providing restorative and oral surgery services. Measuring clinical productivity can be an effective tool to establish benchmarks, improve the site selection process, and educate those skeptical about the benefits of extramural education. Such an evaluation will enable faculty and program administrators involved in CBDEP to make continuous improvements.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".