Responding to the Need for Faculty Development: A Survey of U.S. and Canadian Dental Schools
Bibliographic record
Abstract
The Office of Professional Development at The University of Texas-Houston Health Science Center Dental Branch was established in November 1996 in order to meet the professional development needs of the faculty, staff, and administration. Although other dental schools share similar needs, our research revealed no study to determine how dental schools managed their faculty development needs. Therefore, a preliminary survey to collect data about offices similar to ours was developed and sent to the deans of fifty-four U.S. schools including Puerto Rico and ten Canadian schools. Thirty-seven schools (58 percent) responded, and it was determined that five schools (14 percent) had Offices of Professional Development and seven (19 percent) had Offices of Faculty Affairs. Based on these results, an expanded follow-up survey was conducted. The respondents were asked to indicate 1) which entity within the school was primarily responsible for handling faculty development, and 2) which entity actually sponsored each of eighteen faculty development activities. With a response from thirty-three U.S. schools (61 percent) and six Canadian schools (60 percent), six administrative structures (models) for faculty development were identified: 1) Office of Academic Affairs, 2) Departmental Chair, 3) a Faculty Development Committee, 4) an Office of the Dean, 5) an Office of Faculty/Professional Development, and 6) Other Resources.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".