The status of climate studies in the United States and Canadian dental schools: Deans’ perspectives
Bibliographic record
Abstract
OBJECTIVES: Institutions with a positive cultural climate make community members from all backgrounds valued and included, and treated equitably. Such an environment is optimally suited to prepare future dentists well for leading a diverse team of staff members and addressing the oral health care needs of increasingly more diverse patient populations. The objectives were to assess how many United States and Canadian dental schools had participated in a climate study at their parent institution and/or had conducted their own climate study, which topics these studies had addressed, how they collected their data, from whom they collected data, and how the findings affected these academic units. METHODS: In January 2020, 54 of the 78 dental school deans in the United States and Canada responded to a web-based survey (response rate: 69%). RESULTS: Forty-six parent institutions (85%) and 27 dental schools (50%) had conducted climate studies. Eighty-seven percent of parent institutions assessed the climate overall and the climate for specific groups (70%), such as for persons from underrepresented minority backgrounds (67%) or different religious backgrounds (59%). Most parent institution and dental school studies utilized surveys to collect data from faculty (parent institutions: 76%/dental schools: 96%), staff (74%/93%), administrators (72%/93%), and students (72%/89%). Overall, climate study results positively affected parent institutions' and dental schools' humanistic environment (61%/63%) and the recruitment of faculty (46%/50%), students (46%/46%), and staff (41%/43%). CONCLUSIONS: Climate studies are a widely accepted practice at dental schools and their parent institutions. Their results can play a vital role in shaping the climate of these academic units by fostering efforts to increase diversity, equity, and inclusion.
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 | 0.086 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".