A Look at the Anatomy Educator Job Market: Anatomists Remain in Short Supply
Bibliographic record
Abstract
In 2002, a widely publicized report projected an anatomy educator shortage based on department chairpersons' perceptions. Now, 17 years later, the question lingers: "Does an anatomy educator shortage persist and, if so, how severe is the shortage?" Trends in the number, type, and fill rate of anatomy educator job openings were explored by analyzing job posting in the United States over the past two years. A survey was distributed to leaders of anatomy-related departments in the United States, Canada, and European Union. Most departmental leaders who responded (65% or more) from the United States/Canada (n = 81) and the European Union (n = 52) anticipate they will have "moderate" to "great" difficulty hiring anatomy educators in gross anatomy, histology, and embryology over the next five years. Within the United States, the number of anatomy educator job postings at medical schools more than doubled from at least 21 postings in 2017 to 52 postings in 2018. Twenty-one percent of postings between 2017 and 2018 were never filled. While the number of anatomy educator openings within the United States/Canada is perceived to remain in a steady state for the next five years, the European Union estimates a five-fold increase in the number of openings. Departmental leaders prioritize anatomy educator applicants who have teaching experience (mean ± SD = 4.64 ± 0.84 on five-point Likert scale), versatility in teaching multiple anatomy disciplines (3.93 ± 1.07), and flexibility in implementing various teaching pedagogies (3.69 ± 1.17). Collectively, these data suggest the shortage of anatomy educators continues in the United States/Canada and the European Union.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".