Breaking through the glass ceiling: a survey of promotion rates of graduates of a primary care Faculty Development Fellowship Program.
Bibliographic record
Abstract
BACKGROUND: Academic promotion has been difficult for women and faculty of minority race. We investigated whether completion of a faculty development fellowship would equalize promotion rates of female and minority graduates to those of male and white graduates. METHODS: All graduates of the Michigan State University Primary Care Faculty Development Fellowship Program from 1989-1998 were sent a survey in 1999, which included questions about academic status and appointment. We compared application and follow-up survey data by gender and race/ethnicity. Telephone calls were made to nonrespondents. RESULTS: A total of 175 (88%) graduating fellows responded to the follow-up survey. Information on academic rank at entry and follow-up was obtained from 28 of 48 fellows with missing information on promotion. Male and female graduates achieved similar academic promotion at follow-up, but there was a trend toward lower promotion rates for minority faculty graduates compared to white graduates. In the multivariate analysis, however, only age, years in rank, initial rank, and type of appointment (academic versus clinical) were significant factors for promotion. CONCLUSIONS: Academic advancement is multifactorial and appears most related to time in rank, stage of life, and career choice. Faculty development programs may be most useful in providing skill development and career counseling.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".