Why Are a Quarter of Faculty Considering Leaving Academic Medicine? A Study of Their Perceptions of Institutional Culture and Intentions to Leave at 26 Representative U.S. Medical Schools
Bibliographic record
Abstract
PURPOSE: Vital, productive faculty are critical to academic medicine, yet studies indicate high dissatisfaction and attrition. The authors sought to identify key personal and cultural factors associated with intentions to leave one's institution and/or academic medicine. METHOD: From 2007 through early 2009, the authors surveyed a stratified random sample of 4,578 full-time faculty from 26 representative U.S. medical schools. The survey asked about advancement, engagement, relationships, diversity and equity, leadership, institutional values and practices, and work-life integration. A two-level, multinomial logit model was used to predict leaving intentions. RESULTS: A total of 2,381 faculty responded (52%); 1,994 provided complete data for analysis. Of these, 1,062 (53%) were female and 475 (24%) were underrepresented minorities in medicine. Faculty valued their work, but 273 (14%) had seriously considered leaving their own institution during the prior year and 421 (21%) had considered leaving academic medicine altogether because of dissatisfaction; an additional 109 (5%) cited personal/family issues and 49 (2%) retirement as reasons to leave. Negative perceptions of the culture-unrelatedness, feeling moral distress at work, and lack of engagement-were associated with leaving for dissatisfaction. Other significant predictors were perceptions of values incongruence, low institutional support, and low self-efficacy. Institutional characteristics and personal variables (e.g., gender) were not predictive. CONCLUSIONS: Findings suggest that academic medicine does not support relatedness and a moral culture for many faculty. If these issues are not addressed, academic health centers may find themselves with dissatisfied faculty looking to go elsewhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".