Strategies for Developing a Culture of Mentoring in Postdoctoral Periodontology
Bibliographic record
Abstract
Dental education in the United States and Canada is currently experiencing a crisis with respect to faculty recruitment and retention. The major reasons for the status of dental and specialty education are lower salaries and lack of interest. To make up for this deficit in the specialty of periodontology, our current educators need to utilize strategies targeted towards an existing potential teaching resource: the postdoctoral students in periodontology. The intent of this article is to review the current crisis in dental faculty recruitment and retention, show how it affects the specialty of periodontics, and describe how creating a culture of mentoring may facilitate more engagement of periodontal residents in the teaching process during their postdoctoral training. The strategies utilized to develop a culture of mentoring in the Department of Periodontology at Tufts University are presented. They include methodologies to develop and enhance residents' mentoring skills, thereby helping to compensate for the shortage of periodontists involved in academics. Measurement of the mentoring strategy is presented from data obtained from an online survey of third-year predoctoral students' perceptions of their interaction with residents as their clinical instructors. Moreover, the numbers of program alumni engaged in teaching prior to and after the mentoring program was initiated are also presented as an outcome of the mentoring program. Developing a culture of mentoring in postdoctoral periodontology programs can be an important tool to enable individuals to become more involved in the process of teaching and mentoring during their postdoctoral training. This outcome could ameliorate the deficit of periodontists engaged as formal educators. Moreover, development and expansion of a culture of mentoring in a periodontolgy program may encourage recent graduates to become more involved in teaching subsequent to graduation.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".