Racial and Gender Profile of Public Health Faculty in the United States of America
Bibliographic record
Abstract
Introduction In the context of shifting population demographics in the United States (US), a diverse workforce in the discipline of public health can improve outcomes for various populations through the provision of culturally competent public health policies and corresponding research. This study explored the academic, racial, and gender profile of public health faculty in the USA. Methods In this retrospective cross-sectional analysis, we analyzed the Association of American Medical Colleges (AAMC) annual report of faculty appointments at US medical schools. Descriptive data analysis was performed for chairperson, full professor, associate professor, assistant professor, instructor, and other positions from 2007 to 2018. Results There was a decrease in appointments at all academic ranks from 2007 to 2018 with an absolute change of -239. Overall, most academic positions were occupied by Whites compared to other races, especially in leadership ranks. However, year-by-year analysis showed a gradual decrease in the number of positions held by Whites. Over the last decade, there was a positive trend with a marginally greater number of minorities appointed at academic ranks, specifically Asians. Similarly, no significant change was seen in appointments for Hispanics. Additionally, females occupied a greater number of new positions as compared to their male counterparts except for the higher academic ranks. The data obtained from the AAMC were voluntarily reported and thus may not provide a complete picture of medical faculty in academic medicine. Conclusion Women have shown progress in public health faculty positions during our 12-year study period. However, racial and gender incongruity still exists at higher academic ranks and leadership positions. Further research is warranted to explore factors influencing faculty appointment and promotion, and strategies to reduce inequities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".