Assessment of faculty productivity in academic departments of medicine in the United States: a national survey
Bibliographic record
Abstract
BACKGROUND: Faculty productivity is essential for academic medical centers striving to achieve excellence and national recognition. The objective of this study was to evaluate whether and how academic Departments of Medicine in the United States measure faculty productivity for the purpose of salary compensation. METHODS: We surveyed the Chairs of academic Departments of Medicine in the United States in 2012. We sent a paper-based questionnaire along with a personalized invitation letter by postal mail. For non-responders, we sent reminder letters, then called them and faxed them the questionnaire. The questionnaire included 8 questions with 23 tabulated close-ended items about the types of productivity measured (clinical, research, teaching, administrative) and the measurement strategies used. We conducted descriptive analyses. RESULTS: Chairs of 78 of 152 eligible departments responded to the survey (51% response rate). Overall, 82% of respondents reported measuring at least one type of faculty productivity for the purpose of salary compensation. Amongst those measuring faculty productivity, types measured were: clinical (98%), research (61%), teaching (62%), and administrative (64%). Percentages of respondents who reported the use of standardized measurements units (e.g., Relative Value Units (RVUs)) varied from 17% for administrative productivity to 95% for research productivity. Departments reported a wide variation of what exact activities are measured and how they are monetarily compensated. Most compensation plans take into account academic rank (77%). The majority of compensation plans are in the form of a bonus on top of a fixed salary (66%) and/or an adjustment of salary based on previous period productivity (55%). CONCLUSION: Our survey suggests that most academic Departments of Medicine in the United States measure faculty productivity and convert it into standardized units for the purpose of salary compensation. The exact activities that are measured and how they are monetarily compensated varied substantially across departments.
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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 | 0.020 | 0.281 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".