Evaluating a Leadership Program: A Comparative, Longitudinal Study to Assess the Impact of the Executive Leadership in Academic Medicine (ELAM) Program for Women
Bibliographic record
Abstract
PURPOSE: The Hedwig van Ameringen Executive Leadership in Academic Medicine (ELAM) program provides an external yearlong development program for senior women faculty in U.S. and Canadian medical schools. This study aims to determine the extent to which program participants, compared with women from two comparison groups, aspire to leadership, demonstrate mastery of leadership competencies, and attain leadership positions. METHOD: A pre-/posttest methodology and longitudinal structure were used to evaluate the impact of ELAM participation. Participants from two ELAM cohorts were compared with women who applied but were not accepted into the ELAM program (NON) and women from the Association of American Medical Colleges (AAMC) Faculty Roster. The AAMC group was a baseline for midcareer faculty; the NON group allowed comparison for leadership aspiration. Baseline data were collected in 2002, with follow-up data collected in 2006. Sixteen leadership indicators were considered: administrative leadership attainment (four indicators), full professor academic rank (one), leadership competencies and readiness (eight), and leadership aspirations and education (three). RESULTS: For 15 of the indicators, ELAM participants scored higher than AAMC and NON groups, and for one indicator they scored higher than only the AAMC group (aspiration to leadership outside academic health centers). The differences were statistically significant for 12 indicators and were distributed across the categories. These included seven of the leadership competencies, three of the administrative leadership attainment indicators, and two of the leadership aspirations and education indicators. CONCLUSIONS: These findings support the hypothesis that the ELAM program has a beneficial impact on ELAM fellows in terms of leadership behaviors and career progression.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".