Narratives of Participants in National Career Development Programs for Women in Academic Medicine: Identifying the Opportunities for Strategic Investment
Bibliographic record
Abstract
BACKGROUND: Academic medicine has initiated changes in policy, practice, and programs over the past several decades to address persistent gender disparity and other issues pertinent to its sociocultural context. Three career development programs were implemented to prepare women faculty to succeed in academic medicine: two sponsored by the Association of American Medical Colleges, which began a professional development program for early career women faculty in 1988. By 1995, it had evolved into two programs one for early career women and another for mid-career women. By 2012, more than 4000 women faculty from medical schools across the U.S and Canada had participated in these intensive 3-day programs. The third national program, the Hedwig van Ameringen Executive Leadership in Academic Medicine(®) (ELAM) program for women, was developed in 1995 at the Drexel University College of Medicine. METHODS: Narratives from telephone interviews representing reflections on 78 career development seminars between 1988 and 2010 describe the dynamic relationships between individual, institutional, and sociocultural influences on participants' career advancement. RESULTS: The narratives illuminate the pathway from participating in a career development program to self-defined success in academic medicine in revealing a host of influences that promoted and/or hindered program attendance and participants' ability to benefit after the program in both individual and institutional systems. The context for understanding the importance of these career development programs to women's advancement is nestled in the sociocultural environment, which includes both the gender-related influences and the current status of institutional practices that support women faculty. CONCLUSIONS: The findings contribute to the growing evidence that career development programs, concurrent with strategic, intentional support of institutional leaders, are necessary to achieve gender equity and diversity inclusion.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".