The Myth of the Workforce Crisis. Why the United States Does Not Need More Intensivist Physicians
Bibliographic record
Abstract
Intensivist physician staffing is associated with lower mortality in the intensive care unit (ICU), yet many ICUs are not staffed by trained intensivists. This gap has led to a number of proposals intended to increase the intensivist supply in the United States. In this perspective we argue that such efforts would be both ineffective and ill-advised. Because many ICU patients are not critically ill, workforce models that base demand projections on ICU admission rather than true critical illness substantially overstate the workforce gap. Even in the presence of a workforce gap, training new intensivists would not place them in hospitals where they are needed most, would not mitigate the shortage of nonphysician critical care providers, and would require a unrealistic increase in spending on physician training. In addition, efforts to train more intensivists require us to prioritize intensive care over other specialties that are also in short supply, without clear justification for why intensivists are more important. Rather than continuing an unwarranted push to increase the intensivist supply, we suggest alternative workforce policies that emphasize novel interprofessional care models (to improve ICU quality in the absence of intensivists) combined with limitations on the future growth of ICU beds (to reduce demand through implicit rationing of care). These policies offer opportunities to reduce the mismatch between critical care supply and demand without an unnecessary expansion of the intensivist supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".