Dental School Deans’ Perceptions of the Organizational Culture and Impact of the ELAM Program on the Culture and Advancement of Women Faculty
Bibliographic record
Abstract
In 2006, deans of the sixty-four U.S. and Canadian dental schools were surveyed to gain their perspectives on their institutions' organizational culture for faculty, family-friendly policies, processes used by deans to develop faculty leadership, and the impact of the Executive Leadership in Academic Medicine (ELAM) Program for Women. The deans reported (52 percent response rate) an improved climate in terms of gender equity, yet recognized that inequities still exist. Of fifteen family-friendly policies, only three were available at more than 50 percent of the schools, with little indication that additional policies were under consideration. The deans reported active engagement in behaviors to develop the leadership of their faculty members. Of the nine processes, 50 percent of the deans indicated three they believed to be particularly effective with women. They agreed that ELAM has had a positive impact on their alumnae and their schools. Results are discussed in terms of how the deans' perceptions compare to faculty perceptions and within the larger context of higher education and other organizations. The responsibility of the dean to shape the dental school's culture, particularly in the face of the changing demographics of dental faculty, adds to the importance of the unique perspective provided by the deans.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".