Is there a Perceived Shortage of Anatomy Educators? An International Study
Bibliographic record
Abstract
Purpose In 2002, a widely publicized report projected an anatomy educator shortage based on the perceptions of department chairpersons. Now, 16 years later, with the number of medical and health professions programs higher than ever, does a perceived shortage of anatomy educators (AEs) continue to persist? If there is a shortage, how severe is it and is it a global phenomenon? Methods This study replicated and expanded upon the previously published 2002 report. Two surveys were internationally distributed to 1) departmental leaders and 2) trainees (i.e., graduate students and postdoctoral fellows) within anatomy‐related departments. Trends in the number and type of AE job openings were also explored by analyzing job postings within the US over the past 2 years. Descriptive statistics were used to evaluate perceptions, historic trends, and future projections. Results The majority (51% or more) of departmental leaders who responded from the US/Canada (n=81), the European Union (n=52), and ‘other countries’ (n=26) anticipate they will have ‘moderate’ to ‘great’ difficulty hiring AEs in each of the four classic anatomy disciplines over the next five years. Within the US alone, the number of AE job postings for allopathic and osteopathic medical schools has increased from a minimum of 17 postings in 2017 to 25 postings (and counting) in 2018. While the number of open AE positions within the US/Canada and ‘other countries' is perceived to remain in a steady state over the next 5 years, the European Union estimates a 5 fold increase in the number of openings. Departmental leaders prioritize AE applicants who have teaching experience (90%), the ability to teach multiple anatomy disciplines (72%), and the knowledge/experience of employing different teaching pedagogies (65%). Through the eyes of most (67.2%) trainees, the current job market is perceived to be highly competitive. Conclusions Based on the perceptions of international departmental leaders and trends in documented job postings, the job vacancy gap for AEs continues to widen with the European Union projecting the greatest need for AEs over the next 5 years. Trainees' perceptions that the job market for AEs is competitive might be explained by a mismatch in how AEs are trained and the types of applicants departmental leaders are seeking. Support or Funding Information AAA This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".