Retention of Surgery Specialists in Academia: A Critical Agenda
Bibliographic record
Abstract
Recruitment and retention of specialists to academia appears to be a growing problem in university teaching hospitals. Members of the American College of Veterinary Surgeons (ACVS) believe that the movement of surgery specialists to practice may have an impact on the training of veterinary students and surgery residents. To address these concerns, 1,071 ACVS diplomates and 60 department heads and hospital directors from all veterinary schools in North America were surveyed to determine whether a problem exists, the extent of the problem, and potential reasons for migration of specialists to practice. Responses were obtained from 620 ACVS diplomates (58 per cent) and 38 department heads and hospital directors (63%) from 28 different universities. The responses confirmed a net movement of surgery specialists from academia to practice. Eighty seven percent of department heads and hospital directors believed there was a shortage of small animal surgery specialists in academia; this information was supported by the fact that 47% responded that they had open positions and 68% had difficulty filling positions in the last five years. The demand was slightly less for large animal surgery specialists, and 42 per cent of respondents indicated that they had open positions. Financial considerations were the most common reason for surgery specialists to move from academia to private practice. Seventy-six percent of responding ACVS diplomates in private practice had a total compensation package valued at greater than $125,000 per year, whereas 77.8% of diplomates in academia had total compensation valued at $125,000 or less. Most universities offer starting salaries (not including benefits) for recently certified surgery specialists ranging from $70,000 to $90,000. Reasons for moving from academia to practice besides financial considerations included undesirable location of university hospitals; lack of interest in research; and a belief that university administration was not supportive of surgery specialists. Many academic surgery specialists were frustrated by the requirement for productivity in research, teaching, and service for promotion in tenure-track positions.
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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 | 0.079 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.029 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".