Profile of Department Chairs in U.S. and Canadian Dental Schools: Demographics, Requirements for Success, and Professional Development Needs
Bibliographic record
Abstract
The aim of this survey study was to develop a current profile of department chairs at U.S. and Canadian dental schools. The survey asked respondents to identify their responsibilities; describe the competencies needed to best serve in the position; and assess their needs in terms of professional development. An online survey with 35 items was sent to 754 individuals who self‐identified as department chairs, department heads, or program directors. Overall, 269 responses were received (overall response rate of 35.7%). The results include demographic information, data on length of tenure in the position, predominant responsibilities and challenges faced in the position, competencies necessary for effective service, and an understanding of the needs of department chairs in academic dentistry. This report suggests methods to support the needs of department chairs, including better defining expectations of the position, creating a successful onboarding process, and providing professional development opportunities for chairs. These measures, along with the professional competencies identified as part of the study, will allow administrators to provide more specific support to individuals in essential leadership roles at their institutions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".