Dental faculty shortage in the United States and Canada: are there solutions?
Bibliographic record
Abstract
Over the past several decades, many orthodontic teaching positions have remained unfilled and, despite many efforts, the situation doesn’t seem to change. Currently, there is a need for approximately 300 dental faculty.1 Many of these positions have been open for years and the shortage has reached crisis proportions. To solve this problem, the implicit and explicit requirements regarding hiring and retention of those in dental teaching positions must change. If no one fills these positions, dental programs will not be able to train future dentists properly and public health will suffer. The problem is not only to fill the current open positions, but also to address future needs.2 Most dental faculty are in their 50s and 60s and succession plans seldom exist.3–5 According to estimates, approximately 210 new full-time dental faculty will be needed each year just to maintain current numbers in the United States (6,400 full-time equivalent positions).6–8 In addition to retiring, faculty are also abandoning educational careers to pursue private practice2,8,9 or are planning to do so in the future.10 The reasons are primarily work-related and not necessarily exclusively economic.2,9–11 Currently, part-time faculty are responsible for a large proportion of the duties that were previously carried out by full-time staff.6,8,10 This may be a provisional solution, although disadvantages may include lack of staff involvement in the teaching process. Why are Canadian and American dentists not filling these positions? Among the most important reasons are low salaries compared with private practice earnings; the inability to practise outside the university to supplement income; and the requirements (PhD and expertise in attracting grants) for successful tenure.4,12–14 Several solutions have been proposed.5 For most dental graduates, the decision to enter academia is difficult because of the need to repay the large loans they needed to support their education.6 If more training stipends6,15 were available, student loans would be considerably lower. Salary support is also very important in retaining faculty.10 Other proposed solutions include allowing greater flexibility in moving through academic ranks and making special adjustments for dentistry7,8,16,17; with help from the professional organizations, making academia more attractive to potential immigrants4,15,18; following the medical model,4 whereby faculty can supplement their salary through practices in the school clinics; using federal stipends to subsidize dental training on the condition that the student enters academia for a number of years2,15; temporarily changing the rules to allow foreign-qualified academics to fill the gaps4; soliciting funds from dental graduates to support faculty salaries at their alma mater; and supplementing Dr. Flores-Mir Email: carlosflores@ ualberta.ca �ontact ��uthor Dental Faculty Shortage in the United States and Canada: Are There Solutions?
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".