The Activities and Responsibilities of the Vice Chair for Education in U.S. and Canadian Departments of Medicine
Bibliographic record
Abstract
PURPOSE: A profile of the activities and responsibilities of vice chairs for education is notably absent from the medical education literature. The authors sought to determine the demographics, roles and responsibilities, and major priorities and challenges faced by vice chairs for education. METHOD: In 2010, the authors sent a confidential, Web-based survey to all 82 identified department of medicine vice chairs for education in the United States and Canada. The authors inquired about demographics, roles, expectations of and for their position, opinions on the responsibilities outlined for their position, metrics used to evaluate their success, top priorities, and job descriptions. Analysis included creating descriptive statistics and categorizing the qualitative comments. RESULTS: Fifty-nine vice chairs for education (72%) responded. At the time of appointment, only 6 (10%) were given a job description, and only 17 (28%) had a defined job description and metrics used to evaluate their success. Only 20 (33%) had any formal budget management training, and 23 (38%) controlled an education budget. Five themes emerged regarding the responsibilities and goals of the vice chair for education: oversee educational programs; possess educational expertise; promote educational scholarship; serve in leadership activities; and, disturbingly, respondents found expectations to be vague and ill defined. CONCLUSIONS: Vice chairs for education are departmental leaders. The authors' findings and recommendations can serve as a beginning for defining educational directions and resources, building consensus, and designing an appropriate educational infrastructure for departments of medicine.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".