Survey of Dental Researchers’ Perceptions of Sexual Harassment at AADR Conferences: 2015 to 2018
Bibliographic record
Abstract
= 10,495); examined demographic factors associated with reported experiences; and identified facilitators and potential solutions concerning these types of harassment. Registrants were emailed an invitation to an anonymous online survey. Demographics were assessed categorically, and response distributions to close-ended survey items were evaluated by these variables. Bivariate analyses of participant demographics were conducted with 8 types of perceived harassment. To determine the demographic distribution of reporters, along with bivariate associations among them, restricted analyses were performed among individuals reporting any type of harassment. Qualitative data analysts conducted content analysis of the open-ended responses to questions asking participants to reflect on the topic. Peer debriefing was used to refine the coding schema. A total of 824 responses were received, of which 172 individuals reported experiencing ≥1 of the 8 types of harassment surveyed. Among those, reports of condescending remarks occurred most frequently (70%). Reported harassment of a more sexual nature was less common by comparison. Reporters of harassment were more likely to be women, members of the AADR/CADR (Canadian Association for Dental Research) divisions, and/or frequent meeting attendees. A total of 229 respondents answered at least 1 of the open-ended questions. While the majority of survey respondents reported no personal experience with harassment at AADR meetings, the fact that 1 in 5 did should be cause for concern. In 2018, AADR introduced a "Professional Conduct at Meetings Policy" delineating unacceptable behaviors, including intimidating or harassing speech and actions. Results of this survey form an important baseline from which its impact may be monitored to ensure that future AADR meetings are respectful, supportive, and safe environments for all.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".