FSMB Census of Licensed Physicians in the United States, 2022
Bibliographic record
Abstract
There are 1,044,734 licensed physicians in the United States and District of Columbia, a physician workforce 23% larger than in 2010, based on data supplied by the nation's state medical and osteopathic boards. Despite an impending shortage of physicians nationwide, the licensed physician population has grown relative to the nation's total population, and since the last census in 2020 there have been significant increases in the number of new licenses issued by state medical boards—a trend driven predominantly by the use of telehealth services at levels significantly higher than prior to the COVID-19 pandemic. Nearly one-quarter (24%), or 247,424, of the nation's physicians hold two or more active licenses, up from 23% in 2020, and state medical boards issued a record high of 129,427 new licenses in 2022, an increase of 27% from 2020. A demographic transition towards an older population in the United States is increasing as the demand for healthcare services continues to raise concerns about physician shortages. The physician population is aging alongside the general population, with the number of licensed physicians aged 60 years and older increasing by 54% since our 2010 census. The pandemic exacerbated the strains of an aging population on the entire healthcare system and physician workforce.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".