Academic environment satisfaction and gender—Student perceptions and experiences
Bibliographic record
Abstract
PURPOSE: This study aims to explore gender oral health professions students' perceptions and experiences in the overall academic environment of their school/program and to further explore associations between those perceptions and experiences with overall satisfaction. METHODS: Using data from the ADEA Climate Survey (2022), analysis occurred in two phases: 1) bivariate analysis by gender and 2) multivariable logistic regression for binary overall satisfaction. The model posits overall satisfaction as a function of individual background characteristics and perceptions and experiences in the academic environment. RESULTS: The sample population included responses from 7130 allied programs and dental students from the United States and Canada. Gender differences were noted for School/Program (dental or allied) where the majority of male respondents were dental students (89.9%) and female allied education program students were the majority among female respondents (52.9%). Negative experiences, particularly those involving interpersonal interactions (intimidation, microaggressions, discrimination, and harassment) seem to impact female students more but not exclusively. Considering positive perceptions-Belonging/Community, Supportive School, Authentic Self, and Respect the odds are higher that a student is satisfied in their school/program environment, whereas negative experience lowers the odds of satisfaction. CONCLUSION(S): Initiatives directed toward improving interpersonal interactions and relationships and school student support services are important for students to learn and grow and are key to having quality academic environments. There are roles and opportunities for leadership, offices of students and academic affairs, and faculty and staff to improve and maintain school environments that can lead to overall satisfaction with the school and work environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".