Junior faculty perspectives on the academic environment: A call for development and onboarding
Bibliographic record
Abstract
BACKGROUND: One challenge in dental education is new faculty members can be hired with limited teaching experience or understanding of pedagogy. Recent publications document increasing initiatives of faculty development in US and Canadian dental schools. This study evaluates full-time faculty perspectives of their develop and academic environment. METHODS: A 33-item questionnaire distributed to senior administrators and forwarded to faculty, collected data on demographics and satisfaction, utilizing a 5-item Likert scale, multiple choice, and binary yes/no questions. Responses were descriptively and statistically analyzed utilizing an analysis of variance, chi-square, and two-sample t-test. RESULTS: There were 183 faculty respondents (37.7% male/62.3% female), age ranges were 20-29 (2.2%), 30-39 (53.5%), and 41-49(44.3). Respondents reported race/ethnicity: Caucasian (60.7%), Asian (19.1%), Hispanic (12.6%), Black/African American (5.5%), two or more races (2.2%), and Native American 0%. Length of employment was significantly associated with primary responsibility (p = 0.0023), recent publications (p < 0.0011), and short-term intent to remain in academia (p = 0.046). There was a statistically significant difference between age and satisfaction with professional development (p = 0.0411), achieving career objectives (p = 0.0151), well-being (p = 0.0492), access to resources for scholarly interests (p = 0.0114), communication (p = 0.0058), and assessment training (p = 0.0249). Non-Caucasian faculty reported greater dissatisfaction with being treated respectfully (p = 0.0302), departmental commitment to diversity and inclusion (p = 0.0075), and departmental cooperation/teamwork (p = 0.0323). CONCLUSIONS: A significant number of junior faculty have interest in academic dental careers. Institutions should invest in improving onboarding and professional development. Improve the early experiences of faculty, and foster diversity and inclusion and faculty well-being.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".