Use of Shared Faculty in U.S. and Canadian Dental Schools
Bibliographic record
Abstract
Dental schools are facing substantial financial challenges and a shortage of faculty members. One solution to address these issues has been to hire "shared" faculty members, i.e., faculty members whose primary appointment is at one institution who are hired by another institution to teach a course or part of a course. This is a controversial concept. A survey of academic deans at U.S. and Canadian dental schools was conducted for this study; thirty-nine (54 percent) of the seventy-two academic deans completed the online survey. This survey found that the use of shared faculty members is not rare amongst U.S. and Canadian dental schools and that the opinions of the academic deans about the use of shared faculty members ranged widely-from strong support to strong disapproval. Using shared faculty members has advantages and disadvantages for students, the shared faculty members, and both institutions. Many of the disadvantages could be potentially minimized by stakeholders' working together to develop collaborative arrangements. Networks could be developed in which institutions coordinate hiring of shared faculty members based on what expertise is needed. Financial challenges and shortages of faculty members are unlikely to be resolved in the near future, but use of shared faculty members is one promising approach to begin to meet these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".