Bibliographic record
Abstract
Concerns of a shortage of board certified specialists willing to work in academia have shadowed the medical and veterinary communities for decades. As a result, a number of studies have been conducted to determine how to foster, attract, and retain specialists in academia. More recently, there has been a growing perception that it is difficult for academic institutions to hire board certified veterinary radiologists. The objective of this study was to describe the career paths (academia vs. private sector) of veterinary radiologists and to determine what factors influenced their career path decisions. A mixed mode cross-sectional survey was used to survey ACVR radiologists and residents-in-training, 48% (255/529) of which responded. There was a near unidirectional movement of radiologists from academia to the private sector: 45.7% (59/129) of the respondents who began their careers in academia had switched to the private sector while only 8% (7/88) had left the private sector for academia. If a shortage of academic radiologists exists, then perhaps the issue should be framed as a problem with retention vs. recruitment. The most influential factors in the decision to leave academia were remuneration (wages and benefits), lack of interest/enjoyment in research, geographical location, and family considerations. It is salient that average salaries increased by twofold after leaving academia for the private sector.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".