Bibliographic record
Abstract
ABSTRACT: Health workforce policy in the United States from the mid-1970s has been strongly influenced by perceptions of the adequacy of the physician supply and its relationship to physician assistants/associates (PAs) and NPs. During the 1980s, a series of inaccurate reports by the federal government mistakenly warned of an impending physician surplus and shaped policy decisions for decades. In spite of perceptions of a physician surplus, the PA profession expanded rapidly in the 1990s. Projections of the adequacy of the physician supply changed to a shortage in the first decade of this century and the PA component of the healthcare workforce continued to expand. During the past decade, the Association of American Medical Colleges has employed microsimulation modeling expertise to project the extent of physician shortages, an effort that initially failed to incorporate the contributions of PAs and NPs in the workforce. Although current projection models include the contributions of PAs and NPs, the substitution ratios used are notably low. Specifically, PA and NP productivity effort was set roughly at one-quarter to one-half that of the physician. PAs and NPs make up a substantial contingent within the US healthcare workforce and should be included fully in future workforce projection estimates. This article provides policy recommendations for the advancement of PA contributions to the delivery of medical care.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".