Who would become a successful Dean of Faculty of Medicine: academic or clinician or administrator?
Bibliographic record
Abstract
It has been a long tradition that the medical school dean is an expert in a specialist field with a well-established reputation in research and clinical services. Medical education is no longer simply disease orientated; it is required to put an emphasis on prevention, the need for better management of the health care system, and the need for a better understanding of the sociopolitical aspects of medical care. The deans of medical schools must appreciate the social role of medical education, and the social contract with the community. Although doctors might have difficulties with leadership because they are trained to work as individuals and to value highly their independence and autonomy, good communication skills are an asset for clinicians in management roles. It does not matter whether the background of the dean is academic, clinical or administrative; the most important thing is to possess the managerial skills to tackle the three-way tension between management, academic leadership and professional leadership. The job should be open to people with a good knowledge of and background in health and fiscal expertise, and also a high degree of management, diplomatic and interpersonal skills. Those skills should also be emphasized in the medical curriculum.
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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".