Gender Differences in Pediatric Dentistry Chairs in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: To analyze gender differences in personal and professional demographics, job perceptions and work satisfaction between male and female pediatric dentistry academic leaders in the United States and Canada. STUDY DESIGN: A 40-question survey was sent electronically to department chairs requesting information about demographics, current circumstances of the position, professional history, and opinions about the position. Data was analyzed by the sex of the respondent. RESULTS: Eighty-eight surveys were distributed electronically and 55 chairs responded (response rate: 62.5%). Women comprised 29.5% of the sample, were younger and had less leadership training than men. Men had served longer in the position (t(41)=2.02, p=0.05) and had higher ranking academic titles. Women spent more time managing personnel (p=0.026), creating courses and programs (p=0.029), and teaching (p=0.006) than men. Female chairs perceived to have a difficult relationship with the faculty (p=0.027), felt they received less faculty support (p=0.002), and were significantly more dissatisfied in the job (p=0.037). Men were more stressed about a heavy workload than women (p=0.001). CONCLUSION: Gender was significantly related to the demographics, experience, perceptions of the skills and abilities required for job performance, time management and job satisfaction for pediatric dentistry department chairs in American and Canadian 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".