The 2022 ADEA Climate Study in US and Canadian Dental Schools and Allied Dental Programs—Preliminary Research
Bibliographic record
Abstract
OBJECTIVES: Dental and allied dental educators train future providers that will work in increasingly more diverse environments in which the non-Hispanic White population in the United States will become a minority (47%) by 2050. The objective was to determine the feasibility and need of conducting an ADEA-led climate study of dental schools and allied dental programs in the United States and Canada. Specifically, Aim 1 was to assess the perceptions of deans and program directors of previous climate assessments in their institutions. Aim 2 focused on assessing these academic leaders' considerations concerning a future ADEA-led climate study. Aim 3 was to explore dental and allied dental diversity officers' considerations of the influence of COVID-19 and the Black Lives Matter movement on such a project. METHODS: In 2020, data were collected with two surveys from dental deans, two surveys from allied dental program directors, and two surveys from diversity officers in the United States and Canada. Two focus group studies were also conducted. RESULTS: The perceptions of dental deans and allied dental program directors of previous climate-related research in their institutions differed widely, with a majority agreeing that they would be likely/very likely to participate in an ADEA-led climate study. Concerning such a future climate study, both groups of respondents agreed/agreed strongly that a climate study should collect data from specific groups of dental school and allied dental program members with different social identity characteristics. They also agreed that ADEA should collect information about community members' well-being and stress, sense of belonging, perceptions of discrimination and harassment, and experiences with discrimination and harassment. Findings concerning the effects of COVID-19 on their institutions' climate were mixed. While allied dental program respondents did not consider COVID-19 had a considerable effect, dental school participants perceived a moderate-to-major effect of COVID-19 on their climate. Focus group participants pointed out that resources were scarcer due to COVID-19. CONCLUSIONS: Overall, preliminary literature review results and survey and focus group findings supported and informed plans to conduct an ADEA-led joint dental school and allied dental program climate study in the United States and Canada.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".