Women faculty members’ challenges when pursuing leadership positions in academic dentistry: A survey
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: This study addresses persistent gender disparities in leadership roles in academic dentistry. The objectives were to identify the challenges and barriers to leadership that women face in general and during the COVID-19 pandemic, as well as to describe resources they need to reach their leadership potential. METHODS: The American Dental Education Association Section on Women in Leadership (WIL), gathered survey data concerning leadership challenges and faculty development needs. The survey targeted 300 WIL members in leadership roles or aspiring to leadership roles. It was conducted from January to March 2021 and 128 participants responded (response rate: 42.7%). The data was analyzed quantitatively and qualitatively. RESULTS: The most frequent reported leadership barriers indicated by the survey respondents were lack of mentoring, limited leadership training, lack of sponsorship from administration, family obligations and limited job opportunities. The participants stated that the COVID-19 pandemic exacerbated these challenges, affecting collaborations, increasing stress, and impacting scholarly productivity. The survey responses showed how external factors can challenge faculty productivity. Respondents identified mentoring, increased leadership opportunity awareness, and leadership development seminars as factors most helpful for their professional development. CONCLUSIONS: This study identified women faculty members' perceptions of why disparities persist in gender equity in leadership in dental education. It suggests developing gender specific leadership strategies and resources. Addressing barriers requires concerted efforts at the institutional, national, and global levels. This is vital for achieving gender parity in leadership roles in dental education settings.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".